R Notebook
Sry about the mess, this was mostly exploration
library(here)here() starts at C:/Users/plancha/exoplanetslibrary(tidyverse)── Attaching core tidyverse packages ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4 ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(conflicted)
# library(easystats)
exoplanets <- read_csv(here("data", "exoplanet_catalog_080325.csv"))Warning: One or more parsing issues, call `problems()` on your data frame for details, e.g.:
dat <- vroom(...)
problems(dat)Rows: 7418 Columns: 98── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (12): name, planet_status, publication, detection_type, mass_measurement_type, radius_measurement_type, alternate_names, molecules, star_name, star_sp_type, star_detected_disc, star_alternate_names
dbl (83): mass, mass_error_min, mass_error_max, mass_sini, mass_sini_error_min, mass_sini_error_max, radius, radius_error_min, radius_error_max, orbital_period, orbital_period_error_min, orbital_period_error_max, semi_major_axis, semi_major_axis_error_min, semi_m...
lgl (2): hot_point_lon, star_magnetic_field
date (1): updated
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.exoplanetslibrary(skimr)
skim(exoplanets)Warning: There was 1 warning in `dplyr::summarize()`.
ℹ In argument: `dplyr::across(tidyselect::any_of(variable_names), mangled_skimmers$funs)`.
ℹ In group 0: .
Caused by warning:
! There was 1 warning in `dplyr::summarize()`.
ℹ In argument: `dplyr::across(tidyselect::any_of(variable_names), mangled_skimmers$funs)`.
Caused by warning in `inline_hist()`:
! Variable contains Inf or -Inf value(s) that were converted to NA.── Data Summary ────────────────────────
Values
Name exoplanets
Number of rows 7418
Number of columns 98
_______________________
Column type frequency:
character 12
Date 1
logical 2
numeric 83
________________________
Group variables None library(tidymodels)── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidymodels 1.3.0 ──
✔ broom 1.0.7 ✔ rsample 1.2.1
✔ dials 1.4.0 ✔ tune 1.3.0
✔ infer 1.0.7 ✔ workflows 1.2.0
✔ modeldata 1.4.0 ✔ workflowsets 1.1.0
✔ parsnip 1.3.1 ✔ yardstick 1.3.2
✔ recipes 1.1.1 glimpse(exoplanets)Rows: 7,418
Columns: 98
$ name <chr> "109 Psc b", "112 Psc b", "112 Psc c", "11 Com Ab", "11 UMi b", "14 And Ab", "14 Her b", "14 Her c", "16 Cyg Bb", "18 Del Ab", "1RXS 1609 b", "1RXS J103137.1-690205 b", "1RXS J125608.8-692652 (AB)b", "1RXS J131752.0-505845 b", "1R…
$ planet_status <chr> "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed", "Confirmed…
$ mass <dbl> 5.743, NA, 9.866, NA, NA, NA, 8.500, 7.100, NA, NA, 14.000, 36.500, 44.500, 27.100, 32.000, NA, NA, NA, NA, NA, NA, NA, 19.900, 5.500, NA, 28.100, 37.500, 17.680, 38.000, 33.000, 19.180, 57.500, 14.880, 30.000, 10.000, 24.000, 29.0…
$ mass_error_min <dbl> 0.28900, 0.00500, 1.78100, 1.53491, 1.10000, 0.23000, 0.80000, 0.60000, 0.08000, 0.36000, 3.00000, 2.40000, 5.80000, 4.30000, 6.00000, 6.00000, 0.02000, 0.03000, 0.11000, 0.11000, 0.20000, NA, 5.00000, 0.50000, 0.20000, 1.90000, 2.…
$ mass_error_max <dbl> 1.01100, 0.00400, 3.19000, 1.53491, 1.10000, 0.23000, 1.00000, 1.00000, 0.08000, 0.36000, 2.00000, 2.40000, 5.80000, 4.30000, 6.00000, 6.00000, 0.02000, 0.03000, 0.11000, 0.11000, 0.20000, NA, 5.00000, 0.50000, 1.80000, 9.80000, 2.…
$ mass_sini <dbl> 6.3830, 0.0330, NA, 16.1284, 11.0873, 4.6840, 4.9500, 7.1200, 1.6400, 10.3000, NA, NA, NA, NA, NA, 20.0000, 1.5900, 0.8200, 0.9100, 1.8400, 1.5200, 10.0000, NA, NA, 8.3000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ mass_sini_error_min <dbl> 0.07800, 0.00500, NA, 1.53491, 1.10000, 0.23000, NA, NA, 0.08000, 0.36000, NA, NA, NA, NA, NA, 6.00000, 0.02000, 0.03000, 0.11000, 0.11000, 0.20000, NA, NA, NA, 0.20000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mass_sini_error_max <dbl> 0.07800, 0.00400, NA, 1.53491, 1.10000, 0.23000, NA, NA, 0.08000, 0.36000, NA, NA, NA, NA, NA, 6.00000, 0.02000, 0.03000, 0.11000, 0.11000, 0.20000, NA, NA, NA, 1.80000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ radius <dbl> 1.152, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.700, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.500, NA, NA, 1.570, 1.570, NA, 1.570, 1.220, NA, NA, NA, NA, 1.360, 1.200, 1.430, 0.940, NA, NA, 1.620, 0.940, NA, NA, NA, 1.340, NA, 0…
$ radius_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.560, 1.560, NA, NA, 0.020, NA, NA, NA, NA, 0.040, 0.060, 0.220, 0.160, NA, NA, 0.060, 0.160, NA, NA, NA, 0.130, NA, 0.110, NA, NA…
$ radius_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.570, 1.570, NA, NA, 0.020, NA, NA, NA, NA, 0.040, 0.060, 0.220, 0.160, NA, NA, 0.060, 0.160, NA, NA, NA, 0.130, NA, 0.110, NA, NA…
$ orbital_period <dbl> 1.075400e+03, 4.400000e+00, 3.633670e+04, 3.260300e+02, 5.162200e+02, 1.858400e+02, 1.767560e+03, 5.216000e+04, 7.995000e+02, 9.933000e+02, NA, NA, NA, NA, NA, 3.725000e+03, 2.581900e+02, 5.000000e+03, 3.035060e+01, 4.528000e+02, 8…
$ orbital_period_error_min <dbl> 0.7000, 0.0004, 6039.4000, 0.3200, 3.2500, 0.2300, 0.2200, 1028.0000, 0.6000, 3.2000, NA, NA, NA, NA, NA, 900.0000, 0.0700, 400.0000, 0.0078, 4.5000, 14.0000, NA, NA, NA, 0.0210, NA, 0.0060, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ orbital_period_error_max <dbl> 8.000e-01, 2.000e-04, 9.726e+03, 3.200e-01, 3.250e+00, 2.300e-01, 2.200e-01, 1.028e+03, 6.000e-01, 3.200e+00, NA, NA, NA, NA, NA, 9.000e+02, 7.000e-02, 4.000e+02, 7.800e-03, 2.100e+00, 3.200e+01, NA, NA, NA, 2.100e-02, NA, 6.000e-0…
$ semi_major_axis <dbl> 2.0510, 0.0540, 22.2100, 1.2900, 1.5400, 0.8300, 2.8200, 27.0000, 1.6800, 2.6000, 330.0000, 1725.0000, 503.0000, 5230.0000, 120.0000, 3.9000, 0.8100, 5.8000, 0.1900, 1.3330, 2.0800, 5.9000, NA, 42.0000, NA, NA, 0.0627, 4.4000, 250.…
$ semi_major_axis_error_min <dbl> 0.0870, 0.0020, 2.7660, 0.0500, 0.0700, NA, 0.0400, 7.9000, 0.0300, NA, 0.0000, NA, NA, NA, 20.0000, 1.7000, 0.0200, 0.5000, 0.0090, 0.0090, 0.0200, NA, NA, 2.0000, NA, NA, 0.0014, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.…
$ semi_major_axis_error_max <dbl> 0.0790, 0.0020, 3.8660, 0.0500, 0.0700, NA, 0.0400, 16.0000, 0.0300, NA, 0.0000, NA, NA, NA, 20.0000, 1.7000, 0.0200, 0.5000, 0.0120, 0.0040, 0.0500, NA, NA, 19.0000, NA, NA, 0.0014, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ eccentricity <dbl> 0.104, 0.376, 0.174, 0.231, 0.080, 0.000, 0.372, 0.650, 0.689, 0.080, NA, NA, NA, NA, NA, NA, 0.233, 0.120, 0.042, 0.090, 0.290, NA, NA, NA, 0.681, NA, 0.309, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ eccentricity_error_min <dbl> 0.008, 0.254, 0.154, 0.005, 0.030, NA, 0.003, 0.600, 0.011, 0.010, NA, NA, NA, NA, NA, NA, 0.002, 0.020, 0.029, 0.060, 0.090, NA, NA, NA, 0.053, NA, 0.022, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ eccentricity_error_max <dbl> 0.009, 0.110, 0.110, 0.005, 0.030, NA, 0.003, 0.600, 0.011, 0.010, NA, NA, NA, NA, NA, NA, 0.002, 0.020, 0.048, 0.140, 0.160, NA, NA, NA, 0.053, NA, 0.022, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ inclination <dbl> 86.116, NA, 47.738, NA, NA, NA, 35.700, 82.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 88.500, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ inclination_error_min <dbl> 20.530, NA, 11.804, NA, NA, NA, 3.200, 14.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ inclination_error_max <dbl> 19.957, NA, 12.651, NA, NA, NA, 3.200, 14.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ angular_distance <dbl> 0.066339, NA, NA, 0.011664, 0.012887, 0.010864, 0.153039, 0.381215, 0.078468, 0.035568, 2.275862, NA, NA, NA, NA, NA, 0.036486, 0.261261, NA, 0.017821, 0.027807, NA, NA, 0.877863, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ discovered <dbl> 2000, 2022, 2022, 2007, 2009, 2008, 2002, 2006, 1996, 2008, 2008, 2022, 2024, 2022, 2012, 2012, 1999, 2009, 2018, 2010, 2010, 2009, 2005, 2004, 2021, 2002, 2020, 2019, 2002, 2022, 2010, 2024, 2015, 2024, 2022, 2022, 2003, 2004, 200…
$ updated <date> 2024-06-14, 2024-06-14, 2024-06-14, 2024-08-01, 2023-03-03, 2024-07-30, 2024-10-15, 2024-10-15, 2024-07-28, 2024-08-02, 2024-03-24, 2024-10-24, 2024-06-24, 2024-10-24, 2024-08-04, 2014-10-29, 2024-05-28, 2024-05-28, 2023-08-07, 20…
$ omega <dbl> 112.816, 279.492, 79.772, 94.800, 117.630, NA, 22.300, 0.010, 83.400, 166.100, NA, NA, NA, NA, NA, NA, 252.700, 195.000, 210.000, 9.200, 220.500, NA, NA, NA, NA, NA, -89.900, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ omega_error_min <dbl> 5.448, 67.524, 31.067, 1.500, 21.060, NA, 0.400, 1.000, 2.100, 6.500, NA, NA, NA, NA, NA, NA, 0.500, 48.000, 130.000, 165.400, 320.900, NA, NA, NA, NA, NA, 3.300, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ omega_error_max <dbl> 5.254, 30.206, 15.493, 1.500, 21.060, NA, 0.400, 1.000, 2.100, 6.500, NA, NA, NA, NA, NA, NA, 0.500, 48.000, 100.000, 277.900, 182.200, NA, NA, NA, NA, NA, 3.300, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ tperi <dbl> 2451492, NA, NA, 2452900, 2452861, 2452861, 2451368, 2451779, 2450539, 2451672, NA, NA, NA, NA, NA, NA, 10071, 11100, 2450009, 2454762, 2454930, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ tperi_error_min <dbl> 17.00, NA, NA, 1.60, 2.06, 1.50, 5.00, 33.00, 1.60, 18.00, NA, NA, NA, NA, NA, NA, 0.80, 600.00, 7.90, 172.30, 96.50, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tperi_error_max <dbl> 17.00, NA, NA, 1.60, 2.06, 1.50, 5.00, 33.00, 1.60, 18.00, NA, NA, NA, NA, NA, NA, 0.80, 600.00, 10.10, 172.30, 96.50, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ tconj <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tconj_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tconj_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_sec <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_sec_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_sec_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ lambda_angle <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ lambda_angle_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ lambda_angle_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ impact_parameter <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ impact_parameter_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ impact_parameter_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_vr <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_vr_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_vr_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ k <dbl> 114.583, 4.157, 42.503, 302.800, 189.700, 100.000, 90.380, 50.800, 50.500, 119.400, NA, NA, NA, NA, NA, NA, NA, NA, 59.900, 33.200, 23.500, 80.000, NA, NA, 1070.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ k_error_min <dbl> 1.196, NA, 0.586, 2.600, 7.150, 1.300, 0.150, 0.400, 1.600, 1.300, NA, NA, NA, NA, NA, NA, NA, NA, 3.300, 1.600, 2.900, NA, NA, NA, 170.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ k_error_max <dbl> 1.067, NA, 1.868, 2.600, 7.150, 1.300, 0.150, 0.400, 1.600, 1.300, NA, NA, NA, NA, NA, NA, NA, NA, 3.300, 1.600, 2.900, NA, NA, NA, 1450.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ temp_calculated <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2640.00, NA, NA, 2600.00, NA, NA, 2657.01, 1725.00, 1896.00, NA, NA, NA, 1264.00, 1343.00, 1887.00, 899.00, NA, NA, 1987.00, 836.00, NA, NA, NA…
$ temp_calculated_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, NA, 51, 5, 77, NA, NA, NA, 78, 58, 175, 82, NA, NA, 53, 71, NA, NA, NA, 117, NA, 89, NA, NA, NA, NA, NA, 53, 53, 132, 205,…
$ temp_calculated_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, NA, 51, 5, 77, NA, NA, NA, 78, 58, 175, 82, NA, NA, 53, 71, NA, NA, NA, 117, NA, 89, NA, NA, NA, NA, NA, 52, 53, 132, 205,…
$ temp_measured <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1800, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1300, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ hot_point_lon <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ geometric_albedo <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ geometric_albedo_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ geometric_albedo_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ log_g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.00, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.73, NA, NA, NA, NA, 4.12, 4.60, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4.26, N…
$ publication <chr> "Published in a refereed paper", "Published in a refereed paper", "Published in a refereed paper", "Published in a refereed paper", "Published in a refereed paper", "Published in a refereed paper", "Published in a refereed paper", …
$ detection_type <chr> "Radial Velocity, Astrometry", "Radial Velocity", "Radial Velocity, Astrometry", "Radial Velocity", "Radial Velocity", "Radial Velocity", "Radial Velocity", "Radial Velocity", "Radial Velocity", "Radial Velocity", "Imaging", "Imagi…
$ mass_measurement_type <chr> "Radial Velocity", "Radial Velocity", "Astrometry", "Radial Velocity", "Radial Velocity", "Radial Velocity", "Astrometry", "Astrometry", "Radial Velocity", "Radial Velocity", NA, "Astrometry", "Astrometry", "Astrometry", NA, "Radia…
$ radius_measurement_type <chr> "Theoretical", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Flux", NA, NA, "Primary Transit", "Primary Transit", NA, NA, "Flux", NA, NA, NA, NA, "Flux", "Flux", NA, NA, NA, NA, NA, NA, NA, NA…
$ alternate_names <chr> "HD 10697 b", "HD 12235 b", "HD 12235 c", NA, NA, NA, NA, "HD 145675 c", NA, NA, "1RXS J160929.1-210524 b, 1RXS J1609b, 1RXS J1609-2105b, RXJ 1609", "2MASS J10313710-6901587 b", "2MASS J12560830-6926539 (AB)b", "2MASS J13175314-505…
$ molecules <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "CO, H2O, K", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "CO, Fe, H2O, K, Mg, Na", "CH4, CO, Fe, H2O, He, K, Mg, Na", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_name <chr> "109 Psc", "112 Psc", "112 Psc", "11 Com A", "11 UMi", "14 And A", "14 Her", "14 Her", "16 Cyg B", "18 Del A", "1RXS 1609", "1RXS J103137.1-690205", "1RXS J125608.8-692652", NA, "1RXS J235133.3+312720 A", "1SWASP J1407", "23 Lib", …
$ ra <dbl> 26.2326000, 30.0381648, 30.0381648, 185.1791667, 229.2750000, 352.8208333, 242.5958333, 242.5958333, 295.4625000, 314.6083333, 242.3750000, 157.9046512, 194.0346175, NA, 357.8903083, 211.9500000, 228.3666667, 228.3666667, 217.16250…
$ dec <dbl> 20.0831500, 3.0970140, 3.0970140, 17.7927778, 71.8238889, 39.2361111, 43.8216667, 43.8216667, 50.5175000, 10.8391667, -21.0827778, -69.0329983, -69.4483502, NA, 31.4563947, -39.7619445, -25.3091667, -25.3091667, 49.8450000, -0.9022…
$ mag_v <dbl> 6.290, 5.880, 5.880, 4.740, 5.020, 5.220, 6.670, 6.670, 6.200, 5.520, NA, 11.820, 11.831, NA, 13.600, 12.400, 6.450, 6.450, 5.600, 7.380, 7.380, 4.810, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.000, 7.460, NA, NA, NA, NA, NA, NA, NA, …
$ mag_i <dbl> NA, NA, NA, NA, NA, 4.100, NA, NA, NA, NA, 10.990, NA, 10.048, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ mag_j <dbl> NA, 5.204, 5.204, NA, NA, 3.020, NA, NA, NA, 4.030, 9.820, 10.104, 8.910, NA, NA, NA, NA, NA, NA, NA, NA, 3.250, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9.015, NA, NA, NA, NA, NA, NA, NA, NA, 11.280, NA, NA, NA, 7.500, NA, NA, …
$ mag_h <dbl> NA, 4.630, 4.630, NA, NA, 2.610, NA, NA, NA, 3.440, 9.120, 9.609, 8.250, NA, NA, NA, NA, NA, NA, NA, NA, 2.824, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8.593, NA, NA, NA, NA, NA, NA, NA, NA, 10.700, NA, NA, NA, NA, NA, NA, NA, …
$ mag_k <dbl> NA, 4.494, 4.494, NA, NA, 2.330, NA, NA, NA, 3.670, 8.920, 9.494, 7.990, NA, NA, NA, NA, NA, NA, NA, NA, 2.710, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8.583, NA, NA, NA, NA, NA, NA, NA, NA, 10.400, NA, NA, NA, NA, NA, NA, NA, …
$ star_distance <dbl> 32.5600, 31.7627, 31.7627, 110.6000, 119.5000, 76.4000, 18.1000, 18.1000, 21.4100, 73.1000, 145.0000, 167.6127, 98.6000, NA, NA, 133.0000, 22.2000, 22.2000, 100.0000, 72.2084, 72.2084, 68.4818, 64.6730, 64.6730, NA, 36.6000, 36.600…
$ star_distance_error_min <dbl> 0.88000, 0.10695, 0.10695, 10.50000, 6.90000, 4.10000, NA, NA, 0.24000, 3.50000, 20.00000, NA, NA, NA, NA, 12.00000, 1.10000, 1.10000, 2.50000, 0.68320, 0.68320, 1.71845, 0.48640, 0.48640, NA, 0.30000, 0.30000, 0.30000, 0.30000, 1.…
$ star_distance_error_max <dbl> 0.88000, 0.10695, 0.10695, 10.50000, 6.90000, 4.10000, NA, NA, 0.24000, 3.50000, 20.00000, NA, NA, NA, NA, 12.00000, 1.10000, 1.10000, 2.50000, 0.68320, 0.68320, 1.71845, 0.48640, 0.48640, NA, 0.30000, 0.30000, 0.30000, 0.30000, 1.…
$ star_metallicity <dbl> 0.100, 0.310, 0.310, -0.350, 0.040, -0.240, 0.430, 0.430, 0.080, -0.052, NA, NA, NA, NA, NA, NA, 0.250, 0.250, -0.770, -0.030, -0.030, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_metallicity_error_min <dbl> 0.060, 0.100, 0.100, 0.090, 0.040, NA, 0.080, 0.080, 0.040, 0.023, NA, NA, NA, NA, NA, NA, 0.020, 0.020, 0.030, 0.040, 0.040, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_metallicity_error_max <dbl> 0.060, 0.100, 0.100, 0.090, 0.040, NA, 0.080, 0.080, 0.040, 0.023, NA, NA, NA, NA, NA, NA, 0.020, 0.020, 0.030, 0.040, 0.040, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_mass <dbl> 1.130, 1.100, 1.100, 2.700, 1.800, 2.200, 0.900, 0.900, 1.010, 2.300, 0.730, 0.870, 0.700, NA, 0.450, 0.900, 1.070, 1.070, 0.990, 1.540, 1.540, 2.320, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.170, 0.189, NA, NA, NA, NA, NA, NA, NA…
$ star_mass_error_min <dbl> 0.0300, 0.1330, 0.1330, 0.3000, 0.2500, 0.2000, NA, NA, 0.0400, NA, 0.0500, NA, NA, NA, 0.0500, NA, 0.0800, 0.0800, 0.1300, 0.0800, 0.0800, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.1600, 0.0200, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_mass_error_max <dbl> 0.0300, 0.1330, 0.1330, 0.3000, 0.2500, 0.2000, NA, NA, 0.0400, NA, 0.0500, NA, NA, NA, 0.0500, NA, 0.0800, 0.0800, 0.1900, 0.0800, 0.0800, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.1600, 0.0200, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_radius <dbl> 1.7900, 1.8010, 1.8010, 19.0000, 24.0800, 11.0000, 0.7080, 0.7080, 0.9800, 8.5000, 1.3500, 1.0600, 1.8310, NA, NA, 0.9900, 1.2500, 1.2500, 10.6400, 4.9000, 4.9000, 9.6900, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.1100, 0.2198, NA,…
$ star_radius_error_min <dbl> 0.17000, 0.07250, 0.07250, 2.00000, 1.84000, 1.00000, 0.08500, 0.08500, 0.13000, NA, NA, NA, NA, NA, NA, 0.11000, 0.04000, 0.04000, 0.59000, 0.08000, 0.08000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.05000, 0.00673, NA, NA, NA…
$ star_radius_error_max <dbl> 0.17000, 0.07250, 0.07250, 2.00000, 1.84000, 1.00000, 0.08500, 0.08500, 0.13000, NA, NA, NA, NA, NA, NA, 0.11000, 0.04000, 0.04000, 0.84000, 0.08000, 0.08000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.05000, 0.00673, NA, NA, NA…
$ star_sp_type <chr> "G5 IV", "G0IV", "G0IV", "G8III", "K4III", "K0III", "K0 V", "K0 V", "G2.5 V", "G6III", "K7V", "K2.5IVe", "K7Ve", NA, "M2V", NA, "G5 V", "G5 V", "G3IV", "G5", "G5", "K0III", NA, NA, NA, NA, NA, NA, NA, NA, NA, "M8e D", "F8V", "M5.5V…
$ star_age <dbl> 6.900, NA, NA, NA, 1.560, NA, 5.100, 5.100, 8.000, NA, 0.011, NA, 0.003, NA, 0.100, 0.016, 9.700, 9.700, 6.920, 2.700, 2.700, 1.910, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.045, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_age_error_min <dbl> 0.600, NA, NA, NA, 0.540, NA, NA, NA, 1.800, NA, 0.002, NA, NA, NA, 0.050, NA, 3.700, 3.700, 4.800, 0.400, 0.400, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_age_error_max <dbl> 0.600, NA, NA, NA, 0.540, NA, NA, NA, 1.800, NA, 0.002, NA, NA, NA, 0.050, NA, 3.700, 3.700, 4.800, 0.400, 0.400, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_teff <dbl> 5600.00, 5986.00, 5986.00, 4742.00, 4340.00, 4813.00, 5311.00, 5311.00, 5766.00, 4979.00, 4060.00, 4862.13, 3971.38, NA, NA, 4400.00, 5740.00, 5740.00, 4893.00, 5098.00, 5098.00, 5078.00, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 614…
$ star_teff_error_min <dbl> 80.000, 105.437, 105.437, 100.000, 70.000, 20.000, 87.000, 87.000, 60.000, 18.000, 200.000, NA, NA, NA, NA, 100.000, 23.000, 23.000, 15.000, 44.000, 44.000, 124.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 126.660, 157.000, NA, NA,…
$ star_teff_error_max <dbl> 80.000, 105.437, 105.437, 100.000, 70.000, 20.000, 87.000, 87.000, 60.000, 18.000, 200.000, NA, NA, NA, NA, 100.000, 23.000, 23.000, 15.000, 44.000, 44.000, 124.000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 126.660, 157.000, NA, NA,…
$ star_detected_disc <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_magnetic_field <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_alternate_names <chr> "HD 10697", "HD 12235", "HD 12235", "HD 107383", NA, NA, NA, NA, "HD 186427, GJ 765.1 B", "HD 199665", "1RXS1609, 1RXS J1609, 1RXS J160929.1-210524", "2MASS J10313710-6901587", "2MASS J12560830-6926539", NA, "2MASS J23513366+312722…library(naniar)
gg_miss_var(exoplanets)
library(visdat)
vis_dat(exoplanets)
names(exoplanets) [1] "name" "planet_status" "mass" "mass_error_min" "mass_error_max" "mass_sini" "mass_sini_error_min" "mass_sini_error_max" "radius"
[10] "radius_error_min" "radius_error_max" "orbital_period" "orbital_period_error_min" "orbital_period_error_max" "semi_major_axis" "semi_major_axis_error_min" "semi_major_axis_error_max" "eccentricity"
[19] "eccentricity_error_min" "eccentricity_error_max" "inclination" "inclination_error_min" "inclination_error_max" "angular_distance" "discovered" "updated" "omega"
[28] "omega_error_min" "omega_error_max" "tperi" "tperi_error_min" "tperi_error_max" "tconj" "tconj_error_min" "tconj_error_max" "tzero_tr"
[37] "tzero_tr_error_min" "tzero_tr_error_max" "tzero_tr_sec" "tzero_tr_sec_error_min" "tzero_tr_sec_error_max" "lambda_angle" "lambda_angle_error_min" "lambda_angle_error_max" "impact_parameter"
[46] "impact_parameter_error_min" "impact_parameter_error_max" "tzero_vr" "tzero_vr_error_min" "tzero_vr_error_max" "k" "k_error_min" "k_error_max" "temp_calculated"
[55] "temp_calculated_error_min" "temp_calculated_error_max" "temp_measured" "hot_point_lon" "geometric_albedo" "geometric_albedo_error_min" "geometric_albedo_error_max" "log_g" "publication"
[64] "detection_type" "mass_measurement_type" "radius_measurement_type" "alternate_names" "molecules" "star_name" "ra" "dec" "mag_v"
[73] "mag_i" "mag_j" "mag_h" "mag_k" "star_distance" "star_distance_error_min" "star_distance_error_max" "star_metallicity" "star_metallicity_error_min"
[82] "star_metallicity_error_max" "star_mass" "star_mass_error_min" "star_mass_error_max" "star_radius" "star_radius_error_min" "star_radius_error_max" "star_sp_type" "star_age"
[91] "star_age_error_min" "star_age_error_max" "star_teff" "star_teff_error_min" "star_teff_error_max" "star_detected_disc" "star_magnetic_field" "star_alternate_names" library(janitor)
exoplanets %>% tabyl(planet_status) planet_status n percent
Confirmed 7418 1conflicts_prefer(dplyr::filter)[conflicted] Will prefer dplyr::filter over any other package.exoplanets %>%
filter(name %>% str_like("%TOI-784%"))conflicts_prefer(dplyr::filter)[conflicted] Removing existing preference.[conflicted] Will prefer dplyr::filter over any other package.exoplanets %>%
filter(discovered == 2023)exoplanets %>%
mutate(
ra_rad = ra, # Convert RA to radians
dec_rad = dec # Convert Dec to radians
) %>%
ggplot(aes(x = ra_rad, y = dec_rad, color = dec)) +
geom_point(size = 0.4) +
coord_map("aitoff") + # Apply Aitoff projection
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none" # Optionally remove legend
)
library(dplyr)
library(plotly)
conflicts_prefer(plotly::layout)[conflicted] Will prefer plotly::layout over any other package.# Create a new column to distinguish Kepler exoplanets
exoplanets_3d <- exoplanets %>%
mutate(
ra_rad = ra * pi / 180, # Convert RA from degrees to radians
dec_rad = dec * pi / 180, # Convert Dec from degrees to radians
x = cos(dec_rad) * cos(ra_rad), # Convert to Cartesian coordinates
y = cos(dec_rad) * sin(ra_rad),
z = sin(dec_rad),
color = case_when( # Create a column for red when kepler, blue otherwise
str_detect(paste(name, alternate_names), regex("kepler|koi", ignore_case = TRUE)) ~ "Kepler",
# if it's free floating (star_name is NA)
star_name %>% is.na() ~ "Free Floating",
TRUE ~ "Other"
),
hover_text = paste("Name: ", name) # Create custom hover text with the name of the exoplanet
)
# Define steps for opacity slider
steps <- list(
list(args = list("marker.opacity", 0.0), label = "0.0", method = "restyle"),
list(args = list("marker.opacity", 0.1), label = "0.1", method = "restyle"),
list(args = list("marker.opacity", 0.2), label = "0.2", method = "restyle"),
list(args = list("marker.opacity", 0.3), label = "0.3", method = "restyle"),
list(args = list("marker.opacity", 0.4), label = "0.4", method = "restyle"),
list(args = list("marker.opacity", 0.5), label = "0.5", method = "restyle"),
list(args = list("marker.opacity", 0.6), label = "0.6", method = "restyle"),
list(args = list("marker.opacity", 0.7), label = "0.7", method = "restyle"),
list(args = list("marker.opacity", 0.8), label = "0.8", method = "restyle"),
list(args = list("marker.opacity", 0.9), label = "0.9", method = "restyle"),
list(args = list("marker.opacity", 1.0), label = "1.0", method = "restyle")
)
# Create an interactive 3D scatter plot with plotly
plot_ly(
data = exoplanets_3d,
x = ~x,
y = ~y,
z = ~z,
color = ~color, # Use the kepler_highlight column for color mapping
colors = c("Other" = "red", "Kepler" = "blue", "Free Floating" = "green"),
text = ~hover_text, # Show the name of the exoplanet on hover
type = "scatter3d",
mode = "markers",
marker = list(size = 1, opacity = 0.7), # Default opacity
showlegend = TRUE
) %>%
layout(
title = "3D Sky Map of Exoplanets (Kepler Highlighted)",
scene = list(
xaxis = list(title = "X"),
yaxis = list(title = "Y"),
zaxis = list(title = "Z")
),
sliders = list(
list(
active = 1, # Set the default opacity value to 1.0 (fully opaque)
currentvalue = list(
prefix = "Opacity: ",
font = list(size = 15)
),
pad = list(t = 60),
steps = steps # Use the steps defined earlier for the opacity slider
)
)
)Warning: Ignoring 1 observationsWarning: Ignoring 1 observations
# Assuming your data is loaded as 'exoplanets'
# Convert RA to degrees (if it's in hours:minutes:seconds format)
# If RA is already in degrees, skip this step
exoplanets %>%
mutate(
ra_deg = ra, # Convert RA from hours to degrees (if needed)
# Convert to polar coordinates for plotting
# RA is mapped to theta (0-360 degrees)
theta = ra_deg
) %>%
ggplot(aes(x = theta, y = star_distance, color = mass)) +
# Use coord_polar for circular plot
coord_polar(start = 0, direction = -1) + # Start at 0 degrees, clockwise direction
# Add concentric circles for distance reference
geom_hline(yintercept = c(10, 100, 1000, 10000),
color = "gray", linetype = "solid", size = 0.3, alpha = 0.7) +
# Add radial lines for angle reference
geom_vline(xintercept = seq(0, 330, by = 30),
color = "gray", linetype = "solid", size = 0.3, alpha = 0.7) +
# Plot the exoplanets
geom_point(alpha = 0.8, size = 1) +
# Use log scale for distance
scale_y_log10(
breaks = c(10, 100, 1000, 10000),
labels = c("10 pc", "100 pc", "1000 pc", "10000 pc"),
limits = c(1, 15000)
) +
# Use log scale for mass colors
scale_color_gradientn(
colors = c("#1E90FF", "#32CD32", "#FFFF00", "#FFA500", "#FF4500", "#FF0000"),
trans = "log10",
breaks = c(0.0001, 0.001, 0.01, 0.1, 1, 10),
labels = c("10⁻⁴", "10⁻³", "10⁻²", "10⁻¹", "10⁰", "10¹"),
name = "Planetary Mass (MJup)"
) +
# Remove grid and axis elements
theme_minimal() +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
panel.grid = element_blank(),
legend.position = "bottom",
legend.box = "horizontal",
plot.title = element_text(hjust = 0.5)
) +
ggtitle("Exoplanet Distribution")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.
library(shiny)
library(plotly)
library(dplyr)
library(stringr)
# Assuming 'exoplanets' dataset is available
# Create a new column to distinguish Kepler exoplanets
exoplanets_3d <- exoplanets %>%
mutate(
ra_rad = ra * pi / 180, # Convert RA from degrees to radians
dec_rad = dec * pi / 180, # Convert Dec from degrees to radians
x = cos(dec_rad) * cos(ra_rad), # Convert to Cartesian coordinates
y = cos(dec_rad) * sin(ra_rad),
z = sin(dec_rad),
color = case_when( # Create a column for red when kepler, blue otherwise
str_detect(paste(name, alternate_names), regex("kepler|koi", ignore_case = TRUE)) ~ "Kepler",
# if it's free floating (star_name is NA)
star_name %>% is.na() ~ "Free Floating",
TRUE ~ "Other"
),
hover_text = paste("Name: ", name), # Create custom hover text with the name of the exoplanet
scaled_x = x * (1 / star_distance), # Adjust x coordinate by star distance (closer = closer to center)
scaled_y = y * (1 / star_distance), # Adjust y coordinate similarly
scaled_z = z * (1 / star_distance) # Adjust z coordinate similarly
)
# Define steps for opacity slider
steps <- list(
list(args = list("marker.opacity", 0.0), label = "0.0", method = "restyle"),
list(args = list("marker.opacity", 0.1), label = "0.1", method = "restyle"),
list(args = list("marker.opacity", 0.2), label = "0.2", method = "restyle"),
list(args = list("marker.opacity", 0.3), label = "0.3", method = "restyle"),
list(args = list("marker.opacity", 0.4), label = "0.4", method = "restyle"),
list(args = list("marker.opacity", 0.5), label = "0.5", method = "restyle"),
list(args = list("marker.opacity", 0.6), label = "0.6", method = "restyle"),
list(args = list("marker.opacity", 0.7), label = "0.7", method = "restyle"),
list(args = list("marker.opacity", 0.8), label = "0.8", method = "restyle"),
list(args = list("marker.opacity", 0.9), label = "0.9", method = "restyle"),
list(args = list("marker.opacity", 1.0), label = "1.0", method = "restyle")
)
# Define UI for the Shiny app
ui <- fluidPage(
# Application title
titlePanel("3D Sky Map of Exoplanets (Kepler Highlighted)"),
# Sidebar layout (can remain empty since the slider is in Plotly)
sidebarLayout(
sidebarPanel(
# Empty sidebar panel (since no Shiny slider is needed)
),
mainPanel(
# Plotly output for displaying the plot
plotlyOutput("plot", height = "800px") # Plot height set to 800px
)
)
)
# Define server logic for the Shiny app
server <- function(input, output, session) {
# Create the Plotly figure to be rendered
output$plot <- renderPlotly({
fig <- plot_ly(
data = exoplanets_3d,
x = ~scaled_x,
y = ~scaled_y,
z = ~scaled_z,
color = ~color, # Use the kepler_highlight column for color mapping
colors = c("Other" = "red", "Kepler" = "blue", "Free Floating" = "green"),
text = ~hover_text, # Show the name of the exoplanet on hover
type = "scatter3d",
mode = "markers",
marker = list(size = 2, opacity = 0.7), # Default opacity
showlegend = TRUE
)
# Add the opacity slider directly inside Plotly layout
fig <- fig %>% layout(
title = "3D Sky Map of Exoplanets (Kepler Highlighted)",
scene = list(
xaxis = list(title = "X"),
yaxis = list(title = "Y"),
zaxis = list(title = "Z")
),
sliders = list(
list(
active = 1, # Set the default opacity value to 1.0 (fully opaque)
currentvalue = list(
prefix = "Opacity: ",
font = list(size = 15)
),
pad = list(t = 60),
steps = steps # Use the steps defined earlier for the opacity slider
)
),
height = 800 # Set the height of the plot to 800px
)
fig
})
}
if (F) {
# Run the application
shinyApp(ui = ui, server = server)
}# check how many are missing
exoplanets %>%
select(ra, dec, angular_distance) %>%
mutate(ra = ra %>% is.na(), dec = dec %>% is.na(), angular_distance = angular_distance %>% is.na()) %>%
summarise_all(mean) %>%
gather(key="column", value="percentage")# check which ones dont have ra
exoplanets %>%
filter(ra %>% is.na())# check out alternate names
exoplanets %>%
select(name, alternate_names) %>%
filter(alternate_names %>% str_length() > 0)NAexoplanets %>%
tabyl(publication) publication n percent
Announced on a professional conference 55 0.007414397
Announced on a website 2357 0.317740631
Published in a refereed paper 4873 0.656915611
Submitted to a professional journal 133 0.017929361# remove any column with error in the name
exoplanets_r <- exoplanets %>%
select(-contains("error")) %>%
select(-planet_status, -publication)
exoplanets_r %>% names [1] "name" "mass" "mass_sini" "radius" "orbital_period" "semi_major_axis" "eccentricity" "inclination" "angular_distance" "discovered"
[11] "updated" "omega" "tperi" "tconj" "tzero_tr" "tzero_tr_sec" "lambda_angle" "impact_parameter" "tzero_vr" "k"
[21] "temp_calculated" "temp_measured" "hot_point_lon" "geometric_albedo" "log_g" "detection_type" "mass_measurement_type" "radius_measurement_type" "alternate_names" "molecules"
[31] "star_name" "ra" "dec" "mag_v" "mag_i" "mag_j" "mag_h" "mag_k" "star_distance" "star_metallicity"
[41] "star_mass" "star_radius" "star_sp_type" "star_age" "star_teff" "star_detected_disc" "star_magnetic_field" "star_alternate_names" library(visdat)
vis_dat(exoplanets_r)
vis_miss(exoplanets_r, sort_miss = T, cluster = T)
detection type
exoplanets %>%
tabyl("detection_type") %>%
arrange(-n) detection_type n percent
Primary Transit 4509 0.6078457805
Radial Velocity 1145 0.1543542734
Imaging 922 0.1242922621
Microlensing 313 0.0421946616
Timing 160 0.0215691561
Radial Velocity, Astrometry 99 0.0133459153
Imaging, Astrometry 49 0.0066055541
Astrometry 46 0.0062011324
Imaging, Other 46 0.0062011324
Other 42 0.0056619035
TTV 32 0.0043138312
Timing, Kinematic 10 0.0013480723
Primary Transit, Radial Velocity 7 0.0009436506
Radial Velocity, Primary Transit 7 0.0009436506
Timing, Other 6 0.0008088434
Astrometry, Radial Velocity 3 0.0004044217
Imaging, Other, Kinematic 3 0.0004044217
Imaging, Kinematic 2 0.0002696145
Kinematic 2 0.0002696145
Primary Transit, TTV 2 0.0002696145
Radial Velocity, Imaging 2 0.0002696145
Astrometry, Imaging 1 0.0001348072
Imaging, Other, Astrometry 1 0.0001348072
Imaging, Primary Transit 1 0.0001348072
Imaging, Radial Velocity, Astrometry 1 0.0001348072
Other, Imaging 1 0.0001348072
Other, Imaging, Kinematic 1 0.0001348072
Other, Radial Velocity 1 0.0001348072
Primary Transit, Astrometry 1 0.0001348072
Primary Transit, Kinematic 1 0.0001348072
Radial Velocity, Timing 1 0.0001348072
Timing, Astrometry 1 0.0001348072library(fastDummies)
exoplanets_rd <- exoplanets_r %>%
dummy_cols(select_columns = "detection_type", split = ", ")
exoplanets_rd %>% select(starts_with("detection_type")) %>%
uniqueexoplanets_rd %>%
select(starts_with("detection_type")) %>%
gather(key="detection_type", value="value") %>%
filter(value == 1) %>%
group_by(detection_type) %>%
summarise(n = n(), percentage = n()*100 / nrow(exoplanets_rd)) %>%
arrange(-n)library(naniar)
exoplanets_rd %>%
group_by(`detection_type_Primary Transit`) %>%
miss_var_summary() %>%
arrange(variable) %>%
filter(variable %>% str_detect("detection_type", negate = T)) %>%
ggplot(aes(x = variable, y = pct_miss, fill = `detection_type_Primary Transit`)) +
geom_col(position="dodge") +
coord_flip() 
if (F){
library(misty)
exoplanets_rd %>%
select(tzero_vr, tzero_tr_sec, tzero_tr) %>%
na.test(data = exoplanets_rd)
} # didnt work for some reasonlibrary(shiny)
library(dplyr)
library(plotly)
library(naniar) # Assuming miss_var_summary() is from naniar
# Sample UI
ui <- fluidPage(
titlePanel("Missing Data by Detection Type"),
sidebarLayout(
sidebarPanel(
selectInput("group_var", "Select Detection Type:",
choices = names(exoplanets_rd)[grepl("^detection_type_", names(exoplanets_rd))])
),
mainPanel(
plotlyOutput("missing_plot", height = "700px") # Increased height
)
)
)
# Server function
server <- function(input, output) {
output$missing_plot <- renderPlotly({
exoplanets_rd %>%
# transform vars into bool
mutate(across(starts_with("detection_type"), ~ .x %>% as.logical())) %>%
group_by(.data[[input$group_var]]) %>%
miss_var_summary() %>%
arrange(variable) %>%
filter(!str_detect(variable, "detection_type")) %>%
plot_ly(y = ~variable, x = ~pct_miss, color = ~.data[[input$group_var]], type = "bar") %>%
layout(barmode = "group", height = 700) # Increased plot height
})
}
# Run the app
if (F) {
shinyApp(ui = ui, server = server)
}# filter by the kepler
exoplanets %>%
filter(paste(name, alternate_names) %>% str_like("%Kepler%")) %>%
tabyl("detection_type") detection_type n percent
Other 6 0.0021543986
Primary Transit 2722 0.9773788151
Primary Transit, TTV 2 0.0007181329
Radial Velocity 26 0.0093357271
Radial Velocity, Primary Transit 1 0.0003590664
TTV 23 0.0082585278
Timing 5 0.0017953321# check other
exoplanets %>%
filter(detection_type == "Other")conflicts_prefer(lubridate::yday)[conflicted] Will prefer lubridate::yday over any other package.conflicts_prefer(lubridate::year)[conflicted] Will prefer lubridate::year over any other package.year_with_percentage <- function(date) {
percentage_of_year <- yday(date) / ifelse(leap_year(date), 366, 365)
year(date) + percentage_of_year
}
exoplanets_rd %>%
mutate(updated = updated %>% year_with_percentage) %>%
mutate(diff_disc_updated = updated - discovered) -> exoplanets_rdd
exoplanets_rdd %>%
select(discovered, updated, diff_disc_updated)exoplanets_rddk <- exoplanets_rdd %>%
mutate(is_kepler = paste(name, alternate_names) %>% str_detect("kepler" %>% regex(ignore_case = T)))
exoplanets_rddk %>%
select(name, is_kepler) %>%
arrange(-is_kepler)exoplanets %>%
tabyl(publication) publication n percent
Announced on a professional conference 55 0.007414397
Announced on a website 2357 0.317740631
Published in a refereed paper 4873 0.656915611
Submitted to a professional journal 133 0.017929361Modeling
# transform into is shadow matrix
library(naniar)
exoplanets_rddk %>%
select(-name, -discovered, -updated, -diff_disc_updated, -is_kepler, -star_distance, -starts_with("detection_type")) %>%
janitor::remove_constant() %>%
as_shadow() -> shadow_matrix
# add columns to exoplanets_rd
shadow_exoplanets <- exoplanets_rddk %>%
bind_cols(shadow_matrix) %>%
# select everyone that ends with _NA
select(name, starts_with("detection_type_"), discovered, updated, diff_disc_updated, is_kepler, star_distance, ra, dec, ends_with("_NA")) %>%
# change detection_type to factor
mutate_at(vars(starts_with("detection_type_")), as.factor) %>%
janitor::clean_names()
# TODO reduce dimensionality on the _NA
shadow_exoplanetsmodel
shadow_exoplanets %>% glimpseRows: 7,418
Columns: 58
$ name <chr> "109 Psc b", "112 Psc b", "112 Psc c", "11 Com Ab", "11 UMi b", "14 And Ab", "14 Her b", "14 Her c", "16 Cyg Bb", "18 Del Ab", "1RXS 1609 b", "1RXS J103137.1-690205 b", "1RXS J125608.8-692652 (AB)b", "1RXS J131752.0-505845 b",…
$ detection_type_astrometry <fct> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_imaging <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,…
$ detection_type_radial_velocity <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ detection_type_kinematic <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_other <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_primary_transit <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_microlensing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_ttv <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_timing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ discovered <dbl> 2000, 2022, 2022, 2007, 2009, 2008, 2002, 2006, 1996, 2008, 2008, 2022, 2024, 2022, 2012, 2012, 1999, 2009, 2018, 2010, 2010, 2009, 2005, 2004, 2021, 2002, 2020, 2019, 2002, 2022, 2010, 2024, 2015, 2024, 2022, 2022, 2003, 2004,…
$ updated <dbl> 2024.454, 2024.454, 2024.454, 2024.585, 2023.170, 2024.579, 2024.790, 2024.790, 2024.574, 2024.587, 2024.230, 2024.814, 2024.481, 2024.814, 2024.593, 2014.827, 2024.407, 2024.407, 2023.600, 2021.753, 2021.753, 2024.500, 2024.24…
$ diff_disc_updated <dbl> 24.4535519, 2.4535519, 2.4535519, 17.5846995, 14.1698630, 16.5792350, 22.7896175, 18.7896175, 28.5737705, 16.5874317, 16.2295082, 2.8142077, 0.4808743, 2.8142077, 12.5928962, 2.8273973, 25.4071038, 15.4071038, 5.6000000, 11.753…
$ is_kepler <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
$ star_distance <dbl> 32.5600, 31.7627, 31.7627, 110.6000, 119.5000, 76.4000, 18.1000, 18.1000, 21.4100, 73.1000, 145.0000, 167.6127, 98.6000, NA, NA, 133.0000, 22.2000, 22.2000, 100.0000, 72.2084, 72.2084, 68.4818, 64.6730, 64.6730, NA, 36.6000, 36…
$ ra <dbl> 26.2326000, 30.0381648, 30.0381648, 185.1791667, 229.2750000, 352.8208333, 242.5958333, 242.5958333, 295.4625000, 314.6083333, 242.3750000, 157.9046512, 194.0346175, NA, 357.8903083, 211.9500000, 228.3666667, 228.3666667, 217.1…
$ dec <dbl> 20.0831500, 3.0970140, 3.0970140, 17.7927778, 71.8238889, 39.2361111, 43.8216667, 43.8216667, 50.5175000, 10.8391667, -21.0827778, -69.0329983, -69.4483502, NA, 31.4563947, -39.7619445, -25.3091667, -25.3091667, 49.8450000, -0.…
$ mass_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !…
$ mass_sini_na <fct> !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ radius_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, NA, !NA, !NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, …
$ orbital_period_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ semi_major_axis_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, NA, !NA, !NA, !NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, NA, !NA, NA, !NA, NA, NA,…
$ eccentricity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ inclination_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ angular_distance_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ omega_na <fct> !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ tperi_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ tconj_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_sec_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ lambda_angle_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ impact_parameter_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_vr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ k_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ temp_calculated_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, !N…
$ temp_measured_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ geometric_albedo_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ log_g_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA…
$ mass_measurement_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, !NA, !NA, !…
$ radius_measurement_type_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA,…
$ alternate_names_na <fct> !NA, !NA, !NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, !NA, !NA, NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ molecules_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_name_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, …
$ ra_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA…
$ dec_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA…
$ mag_v_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ mag_i_na <fct> NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ mag_j_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, !NA, NA…
$ mag_h_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA,…
$ mag_k_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, …
$ star_metallicity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_mass_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA…
$ star_radius_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_sp_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA…
$ star_age_na <fct> !NA, NA, NA, NA, !NA, NA, !NA, !NA, !NA, NA, !NA, NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, N…
$ star_teff_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_detected_disc_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_alternate_names_na <fct> !NA, !NA, !NA, !NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, !NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, …library(rpart)
library(dplyr)
library(purrr)
set.seed(123)
# Define target and predictor columns
target_cols <- names(shadow_exoplanets) %>%
keep(~ startsWith(.x, "detection_type_"))
predictor_cols <- names(shadow_exoplanets) %>%
setdiff(c("name", target_cols))
# Train decision trees for each target label
models <- target_cols %>%
set_names() %>%
map(~ rpart(as.formula(paste(.x, "~", paste(predictor_cols, collapse = " + "))),
data = shadow_exoplanets, method = "class"))
# Make predictions and add them to the original dataset
shadow_exoplanets_with_preds <- shadow_exoplanets %>%
bind_cols(models %>%
map_dfc(~ predict(.x, shadow_exoplanets, type = "class")) %>%
rename_with(~ paste0("pred_", target_cols)) # Prefix predictions for clarity
)
predictions <- shadow_exoplanets_with_preds %>%
mutate(
actual_combined = apply(select(., all_of(target_cols)), 1, paste, collapse = "_"),
predicted_combined = apply(select(., starts_with("pred_")), 1, paste, collapse = "_")
) %>% select(actual_combined, predicted_combined, starts_with("pred_"), starts_with("detection_type_"))
predictionsmulti_label_confusion_matrix <- function(y_true, y_pred) {
result <- list()
for (col in names(y_true)) {
confusion_matrix <- table(y_true[[col]], y_pred[[paste0("pred_", col)]])
result[[col]] <- confusion_matrix
}
return(result)
}
multi_label_confusion_matrix(shadow_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))$detection_type_astrometry
0 1
0 7194 22
1 86 116
$detection_type_imaging
0 1
0 6335 53
1 58 972
$detection_type_radial_velocity
0 1
0 6075 77
1 83 1183
$detection_type_kinematic
0 1
0 7399 0
1 19 0
$detection_type_other
0 1
0 7314 3
1 70 31
$detection_type_primary_transit
0 1
0 2785 105
1 70 4458
$detection_type_microlensing
0 1
0 7096 9
1 14 299
$detection_type_ttv
0 1
0 7383 1
1 26 8
$detection_type_timing
0 1
0 7222 18
1 48 130# Load necessary library
library(rpart.plot)
# Plot the decision trees with titles
target_cols %>%
map2(models, ~ {
rpart.plot(.y,
type = 4,
extra = 101,
under = TRUE,
fallen.leaves = TRUE,
main = paste("Decision Tree for", .x)) # Title with the target label
})
[[1]]
[[1]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 202 0 (0.972768940 0.027231060)
2) star_distance>=108.984 5157 26 0 (0.994958309 0.005041691) *
3) star_distance< 108.984 2261 176 0 (0.922158337 0.077841663)
6) mass_na=NA 968 1 0 (0.998966942 0.001033058) *
7) mass_na=!NA 1293 175 0 (0.864655839 0.135344161)
14) radius_na=!NA 690 18 0 (0.973913043 0.026086957) *
15) radius_na=NA 603 157 0 (0.739635158 0.260364842)
30) inclination_na=NA 371 42 0 (0.886792453 0.113207547)
60) updated< 2024.638 260 12 0 (0.953846154 0.046153846) *
61) updated>=2024.638 111 30 0 (0.729729730 0.270270270)
122) mass_measurement_type_na=NA 59 2 0 (0.966101695 0.033898305) *
123) mass_measurement_type_na=!NA 52 24 1 (0.461538462 0.538461538)
246) updated>=2024.642 45 21 0 (0.533333333 0.466666667)
492) dec>=-36.07075 34 12 0 (0.647058824 0.352941176) *
493) dec< -36.07075 11 2 1 (0.181818182 0.818181818) *
247) updated< 2024.642 7 0 1 (0.000000000 1.000000000) *
31) inclination_na=!NA 232 115 0 (0.504310345 0.495689655)
62) updated< 2024.422 53 4 0 (0.924528302 0.075471698) *
63) updated>=2024.422 179 68 1 (0.379888268 0.620111732)
126) diff_disc_updated>=4.168493 90 37 0 (0.588888889 0.411111111)
252) dec>=-47.80806 80 28 0 (0.650000000 0.350000000)
504) omega_na=NA 12 0 0 (1.000000000 0.000000000) *
505) omega_na=!NA 68 28 0 (0.588235294 0.411764706)
1010) ra< 242.6082 51 16 0 (0.686274510 0.313725490)
2020) star_distance< 41.1811 36 7 0 (0.805555556 0.194444444) *
2021) star_distance>=41.1811 15 6 1 (0.400000000 0.600000000) *
1011) ra>=242.6082 17 5 1 (0.294117647 0.705882353) *
253) dec< -47.80806 10 1 1 (0.100000000 0.900000000) *
127) diff_disc_updated< 4.168493 89 15 1 (0.168539326 0.831460674)
254) tperi_na=!NA 11 4 0 (0.636363636 0.363636364) *
255) tperi_na=NA 78 8 1 (0.102564103 0.897435897) *
[[1]]$snipped.nodes
NULL
[[1]]$xlim
[1] 0 1
[[1]]$ylim
[1] 0 1
[[1]]$x
[1] 0.11155452 0.01999938 0.20310965 0.08487998 0.32133933 0.14976057 0.49291809 0.28357680 0.21464117 0.35251243 0.27952176 0.42550310 0.37684265 0.34440236 0.40928295 0.47416354 0.70225939 0.53904414 0.86547463 0.77018126 0.67691540 0.60392473 0.74990607 0.70124563
[25] 0.66880533 0.73368592 0.79856652 0.86344711 0.96076801 0.92832771 0.99320830
[[1]]$y
[1] 0.97521782 0.01347644 0.88778678 0.07086438 0.80035575 0.01347644 0.71292472 0.62549368 0.07086438 0.53806265 0.01347644 0.45063161 0.36320058 0.07086438 0.01347644 0.07086438 0.62549368 0.01347644 0.53806265 0.45063161 0.36320058 0.07086438 0.27576954 0.18833851
[25] 0.01347644 0.07086438 0.01347644 0.07086438 0.45063161 0.01347644 0.07086438
[[1]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
x 0.1115545 0.01999938 0.2031097 0.08487998 0.3213393 0.1497606 0.4929181 0.2835768 0.2146412 0.3525124 0.2795218 0.4255031 0.3768427 0.3444024 0.4092829 0.4741635 0.7022594 0.5390441 0.8654746 0.7701813 0.6769154 0.6039247 0.7499061 0.7012456 0.6688053 0.7336859
NA 0.01999938 0.2031097 0.08487998 0.3213393 0.1497606 0.4929181 0.2835768 0.2146412 0.3525124 0.2795218 0.4255031 0.3768427 0.3444024 0.4092829 0.4741635 0.7022594 0.5390441 0.8654746 0.7701813 0.6769154 0.6039247 0.7499061 0.7012456 0.6688053 0.7336859
NA 0.11155452 0.1115545 0.20310965 0.2031097 0.3213393 0.3213393 0.4929181 0.2835768 0.2835768 0.3525124 0.3525124 0.4255031 0.3768427 0.3768427 0.4255031 0.4929181 0.7022594 0.7022594 0.8654746 0.7701813 0.6769154 0.6769154 0.7499061 0.7012456 0.7012456
[,27] [,28] [,29] [,30] [,31]
x 0.7985665 0.8634471 0.9607680 0.9283277 0.9932083
0.7985665 0.8634471 0.9607680 0.9283277 0.9932083
0.7499061 0.7701813 0.8654746 0.9607680 0.9607680
[[1]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
y 0.9986348 0.03689337 0.9112037 0.09428131 0.8237727 0.03689337 0.7363416 0.6489106 0.09428131 0.5614796 0.03689337 0.4740485 0.3866175 0.09428131 0.03689337 0.09428131 0.6489106 0.03689337 0.5614796 0.4740485 0.3866175 0.09428131 0.2991865 0.2117554 0.03689337
NA 0.98923036 0.9892304 0.90179933 0.9017993 0.81436829 0.8143683 0.7269373 0.63950622 0.6395062 0.55207519 0.5520752 0.4646442 0.37721312 0.37721312 0.46464416 0.7269373 0.63950622 0.6395062 0.5520752 0.4646442 0.37721312 0.3772131 0.2897821 0.20235105
NA 0.98923036 0.9892304 0.90179933 0.9017993 0.81436829 0.8143683 0.7269373 0.63950622 0.6395062 0.55207519 0.5520752 0.4646442 0.37721312 0.37721312 0.46464416 0.7269373 0.63950622 0.6395062 0.5520752 0.4646442 0.37721312 0.3772131 0.2897821 0.20235105
[,26] [,27] [,28] [,29] [,30] [,31]
y 0.09428131 0.03689337 0.09428131 0.4740485 0.03689337 0.09428131
0.20235105 0.28978209 0.46464416 0.5520752 0.46464416 0.46464416
0.20235105 0.28978209 0.46464416 0.5520752 0.46464416 0.46464416
[[1]]$labs
[1] "0\n\n7216 202\n100%" "0\n\n5131 26\n70%" "0\n\n2085 176\n30%" "0\n\n967 1\n13%" "0\n\n1118 175\n17%" "0\n\n672 18\n9%" "0\n\n446 157\n8%" "0\n\n329 42\n5%" "0\n\n248 12\n4%" "0\n\n81 30\n1%" "0\n\n57 2\n1%"
[12] "1\n\n24 28\n1%" "0\n\n24 21\n1%" "0\n\n22 12\n0%" "1\n\n2 9\n0%" "1\n\n0 7\n0%" "0\n\n117 115\n3%" "0\n\n49 4\n1%" "1\n\n68 111\n2%" "0\n\n53 37\n1%" "0\n\n52 28\n1%" "0\n\n12 0\n0%"
[23] "0\n\n40 28\n1%" "0\n\n35 16\n1%" "0\n\n29 7\n0%" "1\n\n6 9\n0%" "1\n\n5 12\n0%" "1\n\n1 9\n0%" "1\n\n15 74\n1%" "0\n\n7 4\n0%" "1\n\n8 70\n1%"
[[1]]$cex
[1] 0.15
[[1]]$boxes
[[1]]$boxes$x1
[1] 0.10858487 0.01702974 0.20014001 0.08191033 0.31836969 0.14679093 0.48994845 0.28060715 0.21167152 0.34954279 0.27655212 0.42253345 0.37387301 0.34143271 0.40631331 0.47119390 0.69928974 0.53607450 0.86250499 0.76721161 0.67394576 0.60095509 0.74693643 0.69827598
[25] 0.66583568 0.73071628 0.79559687 0.86047747 0.95779836 0.92535806 0.99023866
[[1]]$boxes$y1
[1] 0.97982597 0.01808459 0.89239494 0.07547253 0.80496390 0.01808459 0.71753287 0.63010183 0.07547253 0.54267080 0.01808459 0.45523976 0.36780873 0.07547253 0.01808459 0.07547253 0.63010183 0.01808459 0.54267080 0.45523976 0.36780873 0.07547253 0.28037770 0.19294666
[25] 0.01808459 0.07547253 0.01808459 0.07547253 0.45523976 0.01808459 0.07547253
[[1]]$boxes$x2
[1] 0.11452416 0.02296903 0.20607930 0.08784962 0.32430898 0.15273021 0.49588774 0.28654644 0.21761081 0.35548207 0.28249140 0.42847274 0.37981230 0.34737200 0.41225259 0.47713319 0.70522903 0.54201378 0.86844428 0.77315090 0.67988505 0.60689438 0.75287572 0.70421527
[25] 0.67177497 0.73665557 0.80153616 0.86641676 0.96373765 0.93129735 0.99617795
[[1]]$boxes$y2
[1] 0.99863475 0.03689337 0.91120372 0.09428131 0.82377268 0.03689337 0.73634165 0.64891061 0.09428131 0.56147958 0.03689337 0.47404855 0.38661751 0.09428131 0.03689337 0.09428131 0.64891061 0.03689337 0.56147958 0.47404855 0.38661751 0.09428131 0.29918648 0.21175544
[25] 0.03689337 0.09428131 0.03689337 0.09428131 0.47404855 0.03689337 0.09428131
[[1]]$split.labs
[1] ""
[[1]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[1]]$split.box
[[1]]$split.box$x1
[1] -0.000550555 NA 0.070863257 NA 0.134555994 NA 0.264808648 0.199436588 NA 0.245905391 NA 0.360450218 0.333949208 NA NA NA 0.523839562 NA 0.746067749 0.666462256
[21] 0.588720157 NA 0.693168195 0.650631108 NA NA NA NA 0.914904917 NA NA
[[1]]$split.box$y1
[1] 0.9315814 NA 0.8441504 NA 0.7567194 NA 0.6692883 0.5818573 NA 0.4944263 NA 0.4069952 0.3195642 NA NA NA 0.5818573 NA 0.4944263 0.4069952 0.3195642 NA 0.2321332 0.1447021 NA NA
[27] NA NA 0.4069952 NA NA
[[1]]$split.box$x2
[1] 0.04054932 NA 0.09889670 NA 0.16496515 NA 0.30234495 0.22984574 NA 0.31313813 NA 0.39323509 0.35485550 NA NA NA 0.55424872 NA 0.79429477 0.68736855 0.61912931 NA 0.70932306 0.68697955
[25] NA NA NA NA 0.94175050 NA NA
[[1]]$split.box$y2
[1] 0.9503902 NA 0.8629592 NA 0.7755282 NA 0.6880971 0.6006661 NA 0.5132351 NA 0.4258040 0.3383730 NA NA NA 0.6006661 NA 0.5132351 0.4258040 0.3383730 NA 0.2509420 0.1635109 NA NA
[27] NA NA 0.4258040 NA NA
[[2]]
[[2]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1030 0 (0.861148558 0.138851442)
2) star_name_na=!NA 6638 302 0 (0.954504369 0.045495631)
4) orbital_period_na=!NA 6083 55 0 (0.990958409 0.009041591) *
5) orbital_period_na=NA 555 247 0 (0.554954955 0.445045045)
10) star_distance>=413.175 282 3 0 (0.989361702 0.010638298) *
11) star_distance< 413.175 273 29 1 (0.106227106 0.893772894) *
3) star_name_na=NA 780 52 1 (0.066666667 0.933333333)
6) star_distance>=947.5 28 0 0 (1.000000000 0.000000000) *
7) star_distance< 947.5 752 24 1 (0.031914894 0.968085106) *
[[2]]$snipped.nodes
NULL
[[2]]$xlim
[1] 0 1
[[2]]$ylim
[1] 0 1
[[2]]$x
[1] 0.54999629 0.24048427 0.07165954 0.40930901 0.29675918 0.52185883 0.85950830 0.74695848 0.97205813
[[2]]$y
[1] 0.93225602 0.66235050 0.04156779 0.39244497 0.04156779 0.04156779 0.66235050 0.04156779 0.04156779
[[2]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
x 0.5499963 0.2404843 0.07165954 0.4093090 0.2967592 0.5218588 0.8595083 0.7469585 0.9720581
NA 0.2404843 0.07165954 0.4093090 0.2967592 0.5218588 0.8595083 0.7469585 0.9720581
NA 0.5499963 0.24048427 0.2404843 0.4093090 0.4093090 0.5499963 0.8595083 0.8595083
[[2]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
y 1.002436 0.7325308 0.1117481 0.4626252 0.1117481 0.1117481 0.7325308 0.1117481 0.1117481
NA 0.9789253 0.7090198 0.7090198 0.4391143 0.4391143 0.9789253 0.7090198 0.7090198
NA 0.9789253 0.7090198 0.7090198 0.4391143 0.4391143 0.9789253 0.7090198 0.7090198
[[2]]$labs
[1] "0\n\n6388 1030\n100%" "0\n\n6336 302\n89%" "0\n\n6028 55\n82%" "0\n\n308 247\n7%" "0\n\n279 3\n4%" "1\n\n29 244\n4%" "1\n\n52 728\n11%" "0\n\n28 0\n0%" "1\n\n24 728\n10%"
[[2]]$cex
[1] 0.5625
[[2]]$boxes
[[2]]$boxes$x1
[1] 0.54049343 0.23098141 0.06215667 0.39980615 0.28725632 0.51235597 0.85000544 0.73745562 0.96255527
[[2]]$boxes$y1
[1] 0.9554143 0.6855088 0.0647261 0.4156033 0.0647261 0.0647261 0.6855088 0.0647261 0.0647261
[[2]]$boxes$x2
[1] 0.5594991 0.2499871 0.0811624 0.4188119 0.3062620 0.5313617 0.8690112 0.7564613 0.9815610
[[2]]$boxes$y2
[1] 1.0024363 0.7325308 0.1117481 0.4626252 0.1117481 0.1117481 0.7325308 0.1117481 0.1117481
[[2]]$split.labs
[1] ""
[[2]]$split.cex
[1] 1 1 1 1 1 1 1 1 1
[[2]]$split.box
[[2]]$split.box$x1
[1] 0.170994603 -0.007926923 NA 0.224299871 NA NA 0.674499168 NA NA
[[2]]$split.box$y1
[1] 0.8231651 0.5532596 NA 0.2833540 NA NA 0.5532596 NA NA
[[2]]$split.box$x2
[1] 0.3099739 0.1512460 NA 0.3692185 NA NA 0.8194178 NA NA
[[2]]$split.box$y2
[1] 0.8701870 0.6002815 NA 0.3303760 NA NA 0.6002815 NA NA
[[3]]
[[3]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1266 0 (0.829334052 0.170665948)
2) mass_sini_na=NA 6011 138 0 (0.977042090 0.022957910)
4) omega_na=NA 5264 27 0 (0.994870821 0.005129179) *
5) omega_na=!NA 747 111 0 (0.851405622 0.148594378)
10) radius_na=!NA 598 16 0 (0.973244147 0.026755853) *
11) radius_na=NA 149 54 1 (0.362416107 0.637583893)
22) k_na=NA 59 13 0 (0.779661017 0.220338983) *
23) k_na=!NA 90 8 1 (0.088888889 0.911111111) *
3) mass_sini_na=!NA 1407 279 1 (0.198294243 0.801705757)
6) star_distance>=228.4595 256 61 0 (0.761718750 0.238281250)
12) tperi_na=NA 208 21 0 (0.899038462 0.100961538) *
13) tperi_na=!NA 48 8 1 (0.166666667 0.833333333) *
7) star_distance< 228.4595 1151 84 1 (0.072980017 0.927019983)
14) impact_parameter_na=!NA 29 6 0 (0.793103448 0.206896552) *
15) impact_parameter_na=NA 1122 61 1 (0.054367201 0.945632799) *
[[3]]$snipped.nodes
NULL
[[3]]$xlim
[1] 0 1
[[3]]$ylim
[1] 0 1
[[3]]$x
[1] 0.46563942 0.15930738 0.04339796 0.27521680 0.17586587 0.37456774 0.30833378 0.44080169 0.77197147 0.63950356 0.57326960 0.70573751 0.90443938 0.83820542 0.97067333
[[3]]$y
[1] 0.9433406 0.7369702 0.0353107 0.5305998 0.0353107 0.3242293 0.0353107 0.0353107 0.7369702 0.5305998 0.0353107 0.0353107 0.5305998 0.0353107 0.0353107
[[3]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.4656394 0.1593074 0.04339796 0.2752168 0.1758659 0.3745677 0.3083338 0.4408017 0.7719715 0.6395036 0.5732696 0.7057375 0.9044394 0.8382054 0.9706733
NA 0.1593074 0.04339796 0.2752168 0.1758659 0.3745677 0.3083338 0.4408017 0.7719715 0.6395036 0.5732696 0.7057375 0.9044394 0.8382054 0.9706733
NA 0.4656394 0.15930738 0.1593074 0.2752168 0.2752168 0.3745677 0.3745677 0.4656394 0.7719715 0.6395036 0.6395036 0.7719715 0.9044394 0.9044394
[[3]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 1.001037 0.7946661 0.09300664 0.5882957 0.09300664 0.3819253 0.09300664 0.09300664 0.7946661 0.5882957 0.09300664 0.09300664 0.5882957 0.09300664 0.09300664
NA 0.9822278 0.77585735 0.7758573 0.56948691 0.5694869 0.36311647 0.36311647 0.9822278 0.7758573 0.56948691 0.56948691 0.7758573 0.56948691 0.56948691
NA 0.9822278 0.77585735 0.7758573 0.56948691 0.5694869 0.36311647 0.36311647 0.9822278 0.7758573 0.56948691 0.56948691 0.7758573 0.56948691 0.56948691
[[3]]$labs
[1] "0\n\n6152 1266\n100%" "0\n\n5873 138\n81%" "0\n\n5237 27\n71%" "0\n\n636 111\n10%" "0\n\n582 16\n8%" "1\n\n54 95\n2%" "0\n\n46 13\n1%" "1\n\n8 82\n1%" "1\n\n279 1128\n19%" "0\n\n195 61\n3%" "0\n\n187 21\n3%"
[12] "1\n\n8 40\n1%" "1\n\n84 1067\n16%" "0\n\n23 6\n0%" "1\n\n61 1061\n15%"
[[3]]$cex
[1] 0.475
[[3]]$boxes
[[3]]$boxes$x1
[1] 0.45673049 0.15039845 0.03448903 0.26630787 0.16695694 0.36565880 0.29942485 0.43189276 0.76306254 0.63059462 0.56436067 0.69682858 0.89553045 0.82929649 0.96176440
[[3]]$boxes$y1
[1] 0.96341900 0.75704856 0.05538908 0.55067813 0.05538908 0.34430769 0.05538908 0.05538908 0.75704856 0.55067813 0.05538908 0.05538908 0.55067813 0.05538908 0.05538908
[[3]]$boxes$x2
[1] 0.47454836 0.16821631 0.05230689 0.28412574 0.18477480 0.38347667 0.31724271 0.44971062 0.78088040 0.64841249 0.58217853 0.71464644 0.91334831 0.84711435 0.97958226
[[3]]$boxes$y2
[1] 1.00103657 0.79466613 0.09300664 0.58829569 0.09300664 0.38192525 0.09300664 0.09300664 0.79466613 0.58829569 0.09300664 0.09300664 0.58829569 0.09300664 0.09300664
[[3]]$split.labs
[1] ""
[[3]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[3]]$split.box
[[3]]$split.box$x1
[1] 0.101815075 -0.003403628 NA 0.128470353 NA 0.277568270 NA NA 0.577259819 0.533001229 NA NA 0.756362034 NA NA
[[3]]$split.box$y1
[1] 0.8560679 0.6496974 NA 0.4433270 NA 0.2369566 NA NA 0.6496974 0.4433270 NA NA 0.4433270 NA NA
[[3]]$split.box$x2
[1] 0.21679969 0.09019955 NA 0.22326139 NA 0.33909929 NA NA 0.70174729 0.61353797 NA NA 0.92004881 NA NA
[[3]]$split.box$y2
[1] 0.8936854 0.6873150 NA 0.4809446 NA 0.2745741 NA NA 0.6873150 0.4809446 NA NA 0.4809446 NA NA
[[4]]
[[4]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 19 0 (0.997438663 0.002561337) *
[[4]]$snipped.nodes
NULL
[[4]]$xlim
[1] 0 1
[[4]]$ylim
[1] 0 1
[[4]]$x
[1] 0.5
[[4]]$y
[1] 0.5
[[4]]$branch.x
[,1]
x 0.5
NA
NA
[[4]]$branch.y
[,1]
y 0.5
NA
NA
[[4]]$labs
[1] "0\n\n7399 19\n100%"
[[4]]$cex
[1] 1
[[4]]$boxes
[[4]]$boxes$x1
[1] 0.4815882
[[4]]$boxes$y1
[1] 0.5420846
[[4]]$boxes$x2
[1] 0.5184118
[[4]]$boxes$y2
[1] 0.622022
[[4]]$split.labs
[1] ""
[[4]]$split.cex
[1] 1
[[4]]$split.box
[[4]]$split.box$x1
[1] NA
[[4]]$split.box$y1
[1] NA
[[4]]$split.box$x2
[1] NA
[[4]]$split.box$y2
[1] NA
[[5]]
[[5]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 101 0 (0.986384470 0.013615530)
2) diff_disc_updated< 11.64617 6239 34 0 (0.994550409 0.005449591) *
3) diff_disc_updated>=11.64617 1179 67 0 (0.943172180 0.056827820)
6) star_distance< 143.995 791 16 0 (0.979772440 0.020227560) *
7) star_distance>=143.995 388 51 0 (0.868556701 0.131443299)
14) star_distance>=145.3505 353 31 0 (0.912181303 0.087818697)
28) ra>=84.77248 276 11 0 (0.960144928 0.039855072) *
29) ra< 84.77248 77 20 0 (0.740259740 0.259740260)
58) ra< 84.7573 64 7 0 (0.890625000 0.109375000) *
59) ra>=84.7573 13 0 1 (0.000000000 1.000000000) *
15) star_distance< 145.3505 35 15 1 (0.428571429 0.571428571)
30) temp_calculated_na=NA 14 2 0 (0.857142857 0.142857143) *
31) temp_calculated_na=!NA 21 3 1 (0.142857143 0.857142857) *
[[5]]$snipped.nodes
NULL
[[5]]$xlim
[1] 0 1
[[5]]$ylim
[1] 0 1
[[5]]$x
[1] 0.24382224 0.04208274 0.44556175 0.19953796 0.69158553 0.47508460 0.35699318 0.59317602 0.51444841 0.67190363 0.90808646 0.82935885 0.98681408
[[5]]$y
[1] 0.95749510 0.02608106 0.78814709 0.02608106 0.61879909 0.44945108 0.02608106 0.28010307 0.02608106 0.02608106 0.44945108 0.02608106 0.02608106
[[5]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
x 0.2438222 0.04208274 0.4455617 0.1995380 0.6915855 0.4750846 0.3569932 0.5931760 0.5144484 0.6719036 0.9080865 0.8293589 0.9868141
NA 0.04208274 0.4455617 0.1995380 0.6915855 0.4750846 0.3569932 0.5931760 0.5144484 0.6719036 0.9080865 0.8293589 0.9868141
NA 0.24382224 0.2438222 0.4455617 0.4455617 0.6915855 0.4750846 0.4750846 0.5931760 0.5931760 0.6915855 0.9080865 0.9080865
[[5]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
y 0.9996033 0.06818922 0.8302553 0.06818922 0.6609072 0.4915592 0.06818922 0.3222112 0.06818922 0.06818922 0.4915592 0.06818922 0.06818922
NA 0.98549667 0.9854967 0.81614867 0.8161487 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464 0.6468007 0.47745265 0.47745265
NA 0.98549667 0.9854967 0.81614867 0.8161487 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464 0.6468007 0.47745265 0.47745265
[[5]]$labs
[1] "0\n\n7317 101\n100%" "0\n\n6205 34\n84%" "0\n\n1112 67\n16%" "0\n\n775 16\n11%" "0\n\n337 51\n5%" "0\n\n322 31\n5%" "0\n\n265 11\n4%" "0\n\n57 20\n1%" "0\n\n57 7\n1%" "1\n\n0 13\n0%" "1\n\n15 20\n0%"
[12] "0\n\n12 2\n0%" "1\n\n3 18\n0%"
[[5]]$cex
[1] 0.3375
[[5]]$boxes
[[5]]$boxes$x1
[1] 0.23788295 0.03614345 0.43962246 0.19359867 0.68564624 0.46914531 0.35105389 0.58723673 0.50850912 0.66596434 0.90214718 0.82341956 0.98087479
[[5]]$boxes$y1
[1] 0.97139009 0.03997605 0.80204208 0.03997605 0.63269407 0.46334607 0.03997605 0.29399806 0.03997605 0.03997605 0.46334607 0.03997605 0.03997605
[[5]]$boxes$x2
[1] 0.24976153 0.04802202 0.45150103 0.20547725 0.69752482 0.48102389 0.36293247 0.59911531 0.52038769 0.67784292 0.91402575 0.83529814 0.99275336
[[5]]$boxes$y2
[1] 0.99960326 0.06818922 0.83025525 0.06818922 0.66090725 0.49155924 0.06818922 0.32221123 0.06818922 0.06818922 0.49155924 0.06818922 0.06818922
[[5]]$split.labs
[1] ""
[[5]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1
[[5]]$split.box
[[5]]$split.box$x1
[1] -0.002461924 NA 0.160338659 NA 0.433509585 0.340363176 NA 0.500194115 NA NA 0.778874905 NA NA
[[5]]$split.box$y1
[1] 0.8920405 NA 0.7226925 NA 0.5533445 0.3839965 NA 0.2146485 NA NA 0.3839965 NA NA
[[5]]$split.box$x2
[1] 0.0866274 NA 0.2387373 NA 0.5166596 0.3736232 NA 0.5287027 NA NA 0.8798428 NA NA
[[5]]$split.box$y2
[1] 0.9202537 NA 0.7509057 NA 0.5815577 0.4122097 NA 0.2428617 NA NA 0.4122097 NA NA
[[6]]
[[6]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 2890 1 (0.38959288 0.61040712)
2) radius_na=NA 2444 43 0 (0.98240589 0.01759411) *
3) radius_na=!NA 4974 489 1 (0.09831122 0.90168878)
6) orbital_period_na=NA 347 10 0 (0.97118156 0.02881844) *
7) orbital_period_na=!NA 4627 152 1 (0.03285066 0.96714934)
14) discovered< 2006.5 64 17 0 (0.73437500 0.26562500) *
15) discovered>=2006.5 4563 105 1 (0.02301118 0.97698882) *
[[6]]$snipped.nodes
NULL
[[6]]$xlim
[1] 0 1
[[6]]$ylim
[1] 0 1
[[6]]$x
[1] 0.31506792 0.04787934 0.58225650 0.35323772 0.81127529 0.65859610 0.96395447
[[6]]$y
[1] 0.93225602 0.04156779 0.66235050 0.04156779 0.39244497 0.04156779 0.04156779
[[6]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
x 0.3150679 0.04787934 0.5822565 0.3532377 0.8112753 0.6585961 0.9639545
NA 0.04787934 0.5822565 0.3532377 0.8112753 0.6585961 0.9639545
NA 0.31506792 0.3150679 0.5822565 0.5822565 0.8112753 0.8112753
[[6]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
y 1.002436 0.1117481 0.7325308 0.1117481 0.4626252 0.1117481 0.1117481
NA 0.9789253 0.9789253 0.7090198 0.7090198 0.4391143 0.4391143
NA 0.9789253 0.9789253 0.7090198 0.7090198 0.4391143 0.4391143
[[6]]$labs
[1] "1\n\n2890 4528\n100%" "0\n\n2401 43\n33%" "1\n\n489 4485\n67%" "0\n\n337 10\n5%" "1\n\n152 4475\n62%" "0\n\n47 17\n1%" "1\n\n105 4458\n62%"
[[6]]$cex
[1] 0.5625
[[6]]$boxes
[[6]]$boxes$x1
[1] 0.30556506 0.03837648 0.57275364 0.34373486 0.80177242 0.64909324 0.95445161
[[6]]$boxes$y1
[1] 0.9554143 0.0647261 0.6855088 0.0647261 0.4156033 0.0647261 0.0647261
[[6]]$boxes$x2
[1] 0.3245708 0.0573822 0.5917594 0.3627406 0.8207781 0.6680990 0.9734573
[[6]]$boxes$y2
[1] 1.0024363 0.1117481 0.7325308 0.1117481 0.4626252 0.1117481 0.1117481
[[6]]$split.labs
[1] ""
[[6]]$split.cex
[1] 1 1 1 1 1 1 1
[[6]]$split.box
[[6]]$split.box$x1
[1] -0.004386395 NA 0.275433046 NA 0.595639644 NA NA
[[6]]$split.box$y1
[1] 0.8231651 NA 0.5532596 NA 0.2833540 NA NA
[[6]]$split.box$x2
[1] 0.1001451 NA 0.4310424 NA 0.7215525 NA NA
[[6]]$split.box$y2
[1] 0.8701870 NA 0.6002815 NA 0.3303760 NA NA
[[7]]
[[7]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 313 0 (0.9578053384 0.0421946616)
2) star_distance< 2644 7095 65 0 (0.9908386187 0.0091613813)
4) orbital_period_na=!NA 6048 2 0 (0.9996693122 0.0003306878) *
5) orbital_period_na=NA 1047 63 0 (0.9398280802 0.0601719198)
10) star_distance< 908 1018 39 0 (0.9616895874 0.0383104126)
20) updated>=2024.141 894 5 0 (0.9944071588 0.0055928412) *
21) updated< 2024.141 124 34 0 (0.7258064516 0.2741935484)
42) ra< 256.95 82 1 0 (0.9878048780 0.0121951220) *
43) ra>=256.95 42 9 1 (0.2142857143 0.7857142857)
86) ra>=276.3813 9 0 0 (1.0000000000 0.0000000000) *
87) ra< 276.3813 33 0 1 (0.0000000000 1.0000000000) *
11) star_distance>=908 29 5 1 (0.1724137931 0.8275862069) *
3) star_distance>=2644 323 75 1 (0.2321981424 0.7678018576)
6) orbital_period_na=!NA 77 6 0 (0.9220779221 0.0779220779) *
7) orbital_period_na=NA 246 4 1 (0.0162601626 0.9837398374) *
[[7]]$snipped.nodes
NULL
[[7]]$xlim
[1] 0 1
[[7]]$ylim
[1] 0 1
[[7]]$x
[1] 0.59427553 0.26765601 0.03283152 0.50248050 0.28900369 0.16945668 0.40855071 0.30608184 0.51101957 0.44270699 0.57933215 0.71595731 0.92089505 0.85258247 0.98920763
[[7]]$y
[1] 0.96655706 0.82306765 0.01952693 0.67957824 0.53608882 0.01952693 0.39259941 0.01952693 0.24911000 0.01952693 0.01952693 0.01952693 0.82306765 0.01952693 0.01952693
[[7]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.5942755 0.2676560 0.03283152 0.5024805 0.2890037 0.1694567 0.4085507 0.3060818 0.5110196 0.4427070 0.5793322 0.7159573 0.9208951 0.8525825 0.9892076
NA 0.2676560 0.03283152 0.5024805 0.2890037 0.1694567 0.4085507 0.3060818 0.5110196 0.4427070 0.5793322 0.7159573 0.9208951 0.8525825 0.9892076
NA 0.5942755 0.26765601 0.2676560 0.5024805 0.2890037 0.2890037 0.4085507 0.4085507 0.5110196 0.5110196 0.5024805 0.5942755 0.9208951 0.9208951
[[7]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9989317 0.8554423 0.05190155 0.7119529 0.5684634 0.05190155 0.4249740 0.05190155 0.2814846 0.05190155 0.05190155 0.05190155 0.8554423 0.05190155 0.05190155
NA 0.9871762 0.84368678 0.8436868 0.7001974 0.55670795 0.5567079 0.41321854 0.4132185 0.26972912 0.26972912 0.70019736 0.9871762 0.84368678 0.84368678
NA 0.9871762 0.84368678 0.8436868 0.7001974 0.55670795 0.5567079 0.41321854 0.4132185 0.26972912 0.26972912 0.70019736 0.9871762 0.84368678 0.84368678
[[7]]$labs
[1] "0\n\n7105 313\n100%" "0\n\n7030 65\n96%" "0\n\n6046 2\n82%" "0\n\n984 63\n14%" "0\n\n979 39\n14%" "0\n\n889 5\n12%" "0\n\n90 34\n2%" "0\n\n81 1\n1%" "1\n\n9 33\n1%" "0\n\n9 0\n0%" "1\n\n0 33\n0%"
[12] "1\n\n5 24\n0%" "1\n\n75 248\n4%" "0\n\n71 6\n1%" "1\n\n4 242\n3%"
[[7]]$cex
[1] 0.2375
[[7]]$boxes
[[7]]$boxes$x1
[1] 0.59011803 0.26349851 0.02867401 0.49832300 0.28484619 0.16529917 0.40439320 0.30192433 0.50686207 0.43854949 0.57517465 0.71179981 0.91673755 0.84842497 0.98505013
[[7]]$boxes$y1
[1] 0.97542070 0.83193129 0.02839057 0.68844187 0.54495246 0.02839057 0.40146305 0.02839057 0.25797363 0.02839057 0.02839057 0.02839057 0.83193129 0.02839057 0.02839057
[[7]]$boxes$x2
[1] 0.59843303 0.27181351 0.03698902 0.50663800 0.29316119 0.17361418 0.41270821 0.31023934 0.51517708 0.44686450 0.58348966 0.72011482 0.92505255 0.85673998 0.99336513
[[7]]$boxes$y2
[1] 0.99893168 0.85544227 0.05190155 0.71195285 0.56846344 0.05190155 0.42497402 0.05190155 0.28148461 0.05190155 0.05190155 0.05190155 0.85544227 0.05190155 0.05190155
[[7]]$split.labs
[1] ""
[[7]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[7]]$split.box
[[7]]$split.box$x1
[1] 0.236890498 -0.001497568 NA 0.260019965 0.145224381 NA 0.294322045 NA 0.429165418 NA NA NA 0.818253389 NA NA
[[7]]$split.box$y1
[1] 0.9120116 0.7685222 NA 0.6250328 0.4815434 NA 0.3380539 NA 0.1945645 NA NA NA 0.7685222 NA NA
[[7]]$split.box$x2
[1] 0.2984215 0.0671606 NA 0.3179874 0.1936890 NA 0.3178416 NA 0.4562486 NA NA NA 0.8869116 NA NA
[[7]]$split.box$y2
[1] 0.9355226 0.7920332 NA 0.6485437 0.5050543 NA 0.3615649 NA 0.2180755 NA NA NA 0.7920332 NA NA
[[8]]
[[8]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 34 0 (0.995416554 0.004583446)
2) mass_measurement_type_na=NA 4320 6 0 (0.998611111 0.001388889) *
3) mass_measurement_type_na=!NA 3098 28 0 (0.990961911 0.009038089)
6) is_kepler< 0.5 2916 9 0 (0.996913580 0.003086420) *
7) is_kepler>=0.5 182 19 0 (0.895604396 0.104395604)
14) tconj_na=!NA 111 0 0 (1.000000000 0.000000000) *
15) tconj_na=NA 71 19 0 (0.732394366 0.267605634)
30) updated>=2021.458 48 7 0 (0.854166667 0.145833333) *
31) updated< 2021.458 23 11 1 (0.478260870 0.521739130)
62) mag_v_na=!NA 14 4 0 (0.714285714 0.285714286) *
63) mag_v_na=NA 9 1 1 (0.111111111 0.888888889) *
[[8]]$snipped.nodes
NULL
[[8]]$xlim
[1] 0 1
[[8]]$ylim
[1] 0 1
[[8]]$x
[1] 0.24266676 0.06324925 0.42208427 0.24845442 0.59571413 0.43365960 0.75776866 0.61886477 0.89667254 0.80406995 0.98927513
[[8]]$y
[1] 0.95749510 0.02608106 0.78814709 0.02608106 0.61879909 0.02608106 0.44945108 0.02608106 0.28010307 0.02608106 0.02608106
[[8]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
x 0.2426668 0.06324925 0.4220843 0.2484544 0.5957141 0.4336596 0.7577687 0.6188648 0.8966725 0.8040700 0.9892751
NA 0.06324925 0.4220843 0.2484544 0.5957141 0.4336596 0.7577687 0.6188648 0.8966725 0.8040700 0.9892751
NA 0.24266676 0.2426668 0.4220843 0.4220843 0.5957141 0.5957141 0.7577687 0.7577687 0.8966725 0.8966725
[[8]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
y 0.9996033 0.06818922 0.8302553 0.06818922 0.6609072 0.06818922 0.4915592 0.06818922 0.3222112 0.06818922 0.06818922
NA 0.98549667 0.9854967 0.81614867 0.8161487 0.64680066 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464
NA 0.98549667 0.9854967 0.81614867 0.8161487 0.64680066 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464
[[8]]$labs
[1] "0\n\n7384 34\n100%" "0\n\n4314 6\n58%" "0\n\n3070 28\n42%" "0\n\n2907 9\n39%" "0\n\n163 19\n2%" "0\n\n111 0\n1%" "0\n\n52 19\n1%" "0\n\n41 7\n1%" "1\n\n11 12\n0%" "0\n\n10 4\n0%" "1\n\n1 8\n0%"
[[8]]$cex
[1] 0.3375
[[8]]$boxes
[[8]]$boxes$x1
[1] 0.23672747 0.05730996 0.41614499 0.24251513 0.58977484 0.42772031 0.75182937 0.61292549 0.89073325 0.79813066 0.98333584
[[8]]$boxes$y1
[1] 0.97139009 0.03997605 0.80204208 0.03997605 0.63269407 0.03997605 0.46334607 0.03997605 0.29399806 0.03997605 0.03997605
[[8]]$boxes$x2
[1] 0.24860605 0.06918853 0.42802356 0.25439371 0.60165342 0.43959889 0.76370795 0.62480406 0.90261183 0.81000924 0.99521442
[[8]]$boxes$y2
[1] 0.99960326 0.06818922 0.83025525 0.06818922 0.66090725 0.06818922 0.49155924 0.06818922 0.32221123 0.06818922 0.06818922
[[8]]$split.labs
[1] ""
[[8]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1
[[8]]$split.box
[[8]]$split.box$x1
[1] -0.005052566 NA 0.222321555 NA 0.404557087 NA 0.586198691 NA 0.771403868 NA NA
[[8]]$split.box$y1
[1] 0.8920405 NA 0.7226925 NA 0.5533445 NA 0.3839965 NA 0.2146485 NA NA
[[8]]$split.box$x2
[1] 0.1315511 NA 0.2745873 NA 0.4627621 NA 0.6515309 NA 0.8367360 NA NA
[[8]]$split.box$y2
[1] 0.9202537 NA 0.7509057 NA 0.5815577 NA 0.4122097 NA 0.2428617 NA NA
[[9]]
[[9]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 178 0 (0.976004314 0.023995686)
2) star_distance< 2199.205 7073 111 0 (0.984306518 0.015693482)
4) star_metallicity_na=!NA 5214 12 0 (0.997698504 0.002301496) *
5) star_metallicity_na=NA 1859 99 0 (0.946745562 0.053254438)
10) mass_sini_na=NA 1593 33 0 (0.979284369 0.020715631)
20) tperi_na=NA 1569 25 0 (0.984066284 0.015933716) *
21) tperi_na=!NA 24 8 0 (0.666666667 0.333333333)
42) star_distance< 123.45 14 0 0 (1.000000000 0.000000000) *
43) star_distance>=123.45 10 2 1 (0.200000000 0.800000000) *
11) mass_sini_na=!NA 266 66 0 (0.751879699 0.248120301)
22) star_distance< 207.1075 191 6 0 (0.968586387 0.031413613) *
23) star_distance>=207.1075 75 15 1 (0.200000000 0.800000000)
46) diff_disc_updated< 0.4678569 8 2 0 (0.750000000 0.250000000) *
47) diff_disc_updated>=0.4678569 67 9 1 (0.134328358 0.865671642) *
3) star_distance>=2199.205 345 67 0 (0.805797101 0.194202899)
6) orbital_period_na=NA 249 0 0 (1.000000000 0.000000000) *
7) orbital_period_na=!NA 96 29 1 (0.302083333 0.697916667)
14) star_metallicity_na=!NA 18 2 0 (0.888888889 0.111111111) *
15) star_metallicity_na=NA 78 13 1 (0.166666667 0.833333333)
30) angular_distance_na=!NA 7 1 0 (0.857142857 0.142857143) *
31) angular_distance_na=NA 71 7 1 (0.098591549 0.901408451) *
[[9]]$snipped.nodes
NULL
[[9]]$xlim
[1] 0 1
[[9]]$ylim
[1] 0 1
[[9]]$x
[1] 0.49282218 0.19874867 0.04583045 0.35166689 0.21051161 0.13993397 0.28108925 0.23403749 0.32814101 0.49282218 0.42224453 0.56339982 0.51634806 0.61045158 0.78689568 0.70455510 0.86923626 0.79865862 0.93981390 0.89276214 0.98686566
[[9]]$y
[1] 0.95749510 0.78814709 0.02608106 0.61879909 0.44945108 0.02608106 0.28010307 0.02608106 0.02608106 0.44945108 0.02608106 0.28010307 0.02608106 0.02608106 0.78814709 0.02608106 0.61879909 0.02608106 0.44945108 0.02608106 0.02608106
[[9]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21]
x 0.4928222 0.1987487 0.04583045 0.3516669 0.2105116 0.1399340 0.2810893 0.2340375 0.3281410 0.4928222 0.4222445 0.5633998 0.5163481 0.6104516 0.7868957 0.7045551 0.8692363 0.7986586 0.9398139 0.8927621 0.9868657
NA 0.1987487 0.04583045 0.3516669 0.2105116 0.1399340 0.2810893 0.2340375 0.3281410 0.4928222 0.4222445 0.5633998 0.5163481 0.6104516 0.7868957 0.7045551 0.8692363 0.7986586 0.9398139 0.8927621 0.9868657
NA 0.4928222 0.19874867 0.1987487 0.3516669 0.2105116 0.2105116 0.2810893 0.2810893 0.3516669 0.4928222 0.4928222 0.5633998 0.5633998 0.4928222 0.7868957 0.7868957 0.8692363 0.8692363 0.9398139 0.9398139
[[9]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21]
y 0.9996033 0.8302553 0.06818922 0.6609072 0.4915592 0.06818922 0.3222112 0.06818922 0.06818922 0.4915592 0.06818922 0.3222112 0.06818922 0.06818922 0.8302553 0.06818922 0.6609072 0.06818922 0.4915592 0.06818922 0.06818922
NA 0.9854967 0.81614867 0.8161487 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464 0.9854967 0.81614867 0.8161487 0.64680066 0.6468007 0.47745265 0.47745265
NA 0.9854967 0.81614867 0.8161487 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464 0.6468007 0.47745265 0.4774527 0.30810464 0.30810464 0.9854967 0.81614867 0.8161487 0.64680066 0.6468007 0.47745265 0.47745265
[[9]]$labs
[1] "0\n\n7240 178\n100%" "0\n\n6962 111\n95%" "0\n\n5202 12\n70%" "0\n\n1760 99\n25%" "0\n\n1560 33\n21%" "0\n\n1544 25\n21%" "0\n\n16 8\n0%" "0\n\n14 0\n0%" "1\n\n2 8\n0%" "0\n\n200 66\n4%" "0\n\n185 6\n3%"
[12] "1\n\n15 60\n1%" "0\n\n6 2\n0%" "1\n\n9 58\n1%" "0\n\n278 67\n5%" "0\n\n249 0\n3%" "1\n\n29 67\n1%" "0\n\n16 2\n0%" "1\n\n13 65\n1%" "0\n\n6 1\n0%" "1\n\n7 64\n1%"
[[9]]$cex
[1] 0.3375
[[9]]$boxes
[[9]]$boxes$x1
[1] 0.48688289 0.19280938 0.03989116 0.34572761 0.20457232 0.13399468 0.27514996 0.22809820 0.32220173 0.48688289 0.41630525 0.55746053 0.51040877 0.60451229 0.78095639 0.69861581 0.86329697 0.79271933 0.93387462 0.88682285 0.98092638
[[9]]$boxes$y1
[1] 0.97139009 0.80204208 0.03997605 0.63269407 0.46334607 0.03997605 0.29399806 0.03997605 0.03997605 0.46334607 0.03997605 0.29399806 0.03997605 0.03997605 0.80204208 0.03997605 0.63269407 0.03997605 0.46334607 0.03997605 0.03997605
[[9]]$boxes$x2
[1] 0.49876146 0.20468796 0.05176974 0.35760618 0.21645090 0.14587326 0.28702854 0.23997678 0.33408030 0.49876146 0.42818382 0.56933911 0.52228734 0.61639087 0.79283497 0.71049439 0.87517555 0.80459791 0.94575319 0.89870143 0.99280495
[[9]]$boxes$y2
[1] 0.99960326 0.83025525 0.06818922 0.66090725 0.49155924 0.06818922 0.32221123 0.06818922 0.06818922 0.49155924 0.06818922 0.32221123 0.06818922 0.06818922 0.83025525 0.06818922 0.66090725 0.06818922 0.49155924 0.06818922 0.06818922
[[9]]$split.labs
[1] ""
[[9]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[9]]$split.box
[[9]]$split.box$x1
[1] 0.157173656 -0.002871712 NA 0.171906240 0.112613246 NA 0.194838191 NA NA 0.383045234 NA 0.468239824 NA NA 0.658822582 NA 0.749956460 NA 0.839902480 NA
[21] NA
[[9]]$split.box$y1
[1] 0.8920405 0.7226925 NA 0.5533445 0.3839965 NA 0.2146485 NA NA 0.3839965 NA 0.2146485 NA NA 0.7226925 NA 0.5533445 NA 0.3839965 NA NA
[[9]]$split.box$x2
[1] 0.24032369 0.09453261 NA 0.24911698 0.16725469 NA 0.27323679 NA NA 0.46144384 NA 0.56445629 NA NA 0.75028762 NA 0.84736078 NA 0.94562181 NA NA
[[9]]$split.box$y2
[1] 0.9202537 0.7509057 NA 0.5815577 0.4122097 NA 0.2428617 NA NA 0.4122097 NA 0.2428617 NA NA 0.7509057 NA 0.5815577 NA 0.4122097 NA NA
# get accuracy
accuracy <- function(y_true, y_pred) {
result <- list()
for (col in names(y_true)) {
confusion_matrix <- table(y_true[[col]], y_pred[[paste0("pred_", col)]])
result[[col]] <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
}
return(result)
}
accuracy(shadow_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))$detection_type_astrometry
[1] 0.9854408
$detection_type_imaging
[1] 0.9850364
$detection_type_radial_velocity
[1] 0.9784308
$detection_type_kinematic
[1] 0.9974387
$detection_type_other
[1] 0.9901591
$detection_type_primary_transit
[1] 0.9764087
$detection_type_microlensing
[1] 0.9968994
$detection_type_ttv
[1] 0.9963602
$detection_type_timing
[1] 0.9911027set.seed(123)
shadow_exoplanets %>%
select(-name, -starts_with("detection_type"), -ends_with("_na"), -discovered, -updated, -diff_disc_updated, -is_kepler) %>%
as_shadow() %>%
bind_cols(shadow_exoplanets) %>%
select(-discovered, -updated, -diff_disc_updated, -is_kepler, -star_distance, -ra, -dec) %>%
relocate(name, starts_with("detection_type")) -> shadower_exoplanets
shadower_exoplanets %>% glimpseRows: 7,418
Columns: 54
$ name <chr> "109 Psc b", "112 Psc b", "112 Psc c", "11 Com Ab", "11 UMi b", "14 And Ab", "14 Her b", "14 Her c", "16 Cyg Bb", "18 Del Ab", "1RXS 1609 b", "1RXS J103137.1-690205 b", "1RXS J125608.8-692652 (AB)b", "1RXS J131752.0-505845 b",…
$ detection_type_astrometry <fct> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_imaging <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,…
$ detection_type_radial_velocity <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ detection_type_kinematic <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_other <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_primary_transit <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_microlensing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_ttv <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ detection_type_timing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ star_distance_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !N…
$ ra_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA…
$ dec_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA…
$ mass_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !…
$ mass_sini_na <fct> !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ radius_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, NA, !NA, !NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, …
$ orbital_period_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ semi_major_axis_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, NA, !NA, !NA, !NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, NA, !NA, NA, !NA, NA, NA,…
$ eccentricity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ inclination_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ angular_distance_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ omega_na <fct> !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ tperi_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ tconj_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_tr_sec_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ lambda_angle_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ impact_parameter_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ tzero_vr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ k_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ temp_calculated_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, !N…
$ temp_measured_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ geometric_albedo_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ log_g_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA…
$ mass_measurement_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, !NA, !NA, !…
$ radius_measurement_type_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA,…
$ alternate_names_na <fct> !NA, !NA, !NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, !NA, !NA, NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ molecules_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_name_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, …
$ ra_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA…
$ dec_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA…
$ mag_v_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ mag_i_na <fct> NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ mag_j_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, !NA, NA…
$ mag_h_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA,…
$ mag_k_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, …
$ star_metallicity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_mass_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA…
$ star_radius_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_sp_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA…
$ star_age_na <fct> !NA, NA, NA, NA, !NA, NA, !NA, !NA, !NA, NA, !NA, NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, N…
$ star_teff_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ star_detected_disc_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ star_alternate_names_na <fct> !NA, !NA, !NA, !NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, !NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, …# Do the same thing with shadower_exoplanets
# Define target and predictor columns
target_cols <- names(shadower_exoplanets) %>%
keep(~ startsWith(.x, "detection_type_"))
predictor_cols <- names(shadower_exoplanets) %>%
setdiff(c("name", target_cols))
# Train decision trees for each target label
models <- target_cols %>%
set_names() %>%
map(~ rpart(as.formula(paste(.x, "~", paste(predictor_cols, collapse = " + "))),
data = shadower_exoplanets, method = "class"))
# Make predictions and add them to the original dataset
shadower_exoplanets_with_preds <- shadower_exoplanets %>%
bind_cols(models %>%
map_dfc(~ predict(.x, shadower_exoplanets, type = "class")) %>%
rename_with(~ paste0("pred_", target_cols)) # Prefix predictions for clarity
)
predictions <- shadower_exoplanets_with_preds %>%
mutate(
actual_combined = apply(select(., all_of(target_cols)), 1, paste, collapse = "_"),
predicted_combined = apply(select(., starts_with("pred_")), 1, paste, collapse = "_")
) %>% select(actual_combined, predicted_combined, starts_with("pred_"), starts_with("detection_type_"))
predictions# accuracy
accuracy(shadower_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))$detection_type_astrometry
[1] 0.9791049
$detection_type_imaging
[1] 0.9766783
$detection_type_radial_velocity
[1] 0.9800485
$detection_type_kinematic
[1] 0.9974387
$detection_type_other
[1] 0.9863845
$detection_type_primary_transit
[1] 0.9723645
$detection_type_microlensing
[1] 0.9912375
$detection_type_ttv
[1] 0.995821
$detection_type_timing
[1] 0.9889458# plot
target_cols %>%
map2(models, ~ {
rpart.plot(.y,
type = 4,
extra = 101,
under = TRUE,
fallen.leaves = TRUE,
main = paste("Decision Tree for", .x)) # Title with the target label
}) -> plots
plots[[1]]
[[1]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 202 0 (0.972768940 0.027231060)
2) radius_na=!NA 4974 21 0 (0.995778046 0.004221954) *
3) radius_na=NA 2444 181 0 (0.925941080 0.074058920)
6) inclination_na=NA 2146 52 0 (0.975768872 0.024231128) *
7) inclination_na=!NA 298 129 0 (0.567114094 0.432885906)
14) mag_k_na=NA 198 62 0 (0.686868687 0.313131313) *
15) mag_k_na=!NA 100 33 1 (0.330000000 0.670000000)
30) mass_measurement_type_na=NA 7 0 0 (1.000000000 0.000000000) *
31) mass_measurement_type_na=!NA 93 26 1 (0.279569892 0.720430108)
62) alternate_names_na=!NA 29 14 1 (0.482758621 0.517241379)
124) mass_sini_na=!NA 10 2 0 (0.800000000 0.200000000) *
125) mass_sini_na=NA 19 6 1 (0.315789474 0.684210526) *
63) alternate_names_na=NA 64 12 1 (0.187500000 0.812500000) *
[[1]]$snipped.nodes
NULL
[[1]]$xlim
[1] 0 1
[[1]]$ylim
[1] 0 1
[[1]]$x
[1] 0.18798605 0.02519211 0.35077999 0.18548153 0.51607846 0.34577095 0.68638596 0.50606037 0.86671156 0.74649449 0.66634978 0.82663920 0.98692862
[[1]]$y
[1] 0.96489496 0.02130589 0.82192692 0.02130589 0.67895888 0.02130589 0.53599084 0.02130589 0.39302280 0.25005475 0.02130589 0.02130589 0.02130589
[[1]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
x 0.1879861 0.02519211 0.3507800 0.1854815 0.5160785 0.3457709 0.6863860 0.5060604 0.8667116 0.7464945 0.6663498 0.8266392 0.9869286
NA 0.02519211 0.3507800 0.1854815 0.5160785 0.3457709 0.6863860 0.5060604 0.8667116 0.7464945 0.6663498 0.8266392 0.9869286
NA 0.18798605 0.1879861 0.3507800 0.3507800 0.5160785 0.5160785 0.6863860 0.6863860 0.8667116 0.7464945 0.7464945 0.8667116
[[1]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
y 0.9991739 0.0555848 0.8562058 0.0555848 0.7132378 0.0555848 0.5702697 0.0555848 0.4273017 0.2843337 0.0555848 0.0555848 0.0555848
NA 0.9874746 0.9874746 0.8445065 0.8445065 0.7015385 0.7015385 0.5585705 0.5585705 0.4156024 0.2726344 0.2726344 0.4156024
NA 0.9874746 0.9874746 0.8445065 0.8445065 0.7015385 0.7015385 0.5585705 0.5585705 0.4156024 0.2726344 0.2726344 0.4156024
[[1]]$labs
[1] "0\n\n7216 202\n100%" "0\n\n4953 21\n67%" "0\n\n2263 181\n33%" "0\n\n2094 52\n29%" "0\n\n169 129\n4%" "0\n\n136 62\n3%"
[7] "1\n\n33 67\n1%" "0\n\n7 0\n0%" "1\n\n26 67\n1%" "1\n\n14 15\n0%" "0\n\n8 2\n0%" "1\n\n6 13\n0%"
[13] "1\n\n12 52\n1%"
[[1]]$cex
[1] 0.275
[[1]]$boxes
[[1]]$boxes$x1
[1] 0.18304371 0.02024977 0.34583765 0.18053919 0.51113612 0.34082861 0.68144362 0.50111803 0.86176922 0.74155215 0.66140744 0.82169686 0.98198628
[[1]]$boxes$y1
[1] 0.97577530 0.03218622 0.83280726 0.03218622 0.68983922 0.03218622 0.54687117 0.03218622 0.40390313 0.26093509 0.03218622 0.03218622 0.03218622
[[1]]$boxes$x2
[1] 0.19292839 0.03013445 0.35572233 0.19042387 0.52102079 0.35071329 0.69132830 0.51100271 0.87165390 0.75143683 0.67129212 0.83158154 0.99187096
[[1]]$boxes$y2
[1] 0.9991739 0.0555848 0.8562058 0.0555848 0.7132378 0.0555848 0.5702697 0.0555848 0.4273017 0.2843337 0.0555848 0.0555848 0.0555848
[[1]]$split.labs
[1] ""
[[1]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1
[[1]]$split.box
[[1]]$split.box$x1
[1] -0.001002289 NA 0.153356321 NA 0.319576547 NA 0.449223459 NA 0.703496137 0.633730342 NA NA
[13] NA
[[1]]$split.box$y1
[1] 0.9106103 NA 0.7676422 NA 0.6246742 NA 0.4817061 NA 0.3387381 0.1957701 NA NA NA
[[1]]$split.box$x2
[1] 0.05138651 NA 0.21760674 NA 0.37196535 NA 0.56289727 NA 0.78949285 0.69896923 NA NA NA
[[1]]$split.box$y2
[1] 0.9340088 NA 0.7910408 NA 0.6480728 NA 0.5051047 NA 0.3621367 0.2191686 NA NA NA
[[2]]
[[2]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1030 0 (0.861148558 0.138851442)
2) star_name_na=!NA 6638 302 0 (0.954504369 0.045495631)
4) orbital_period_na=!NA 6083 55 0 (0.990958409 0.009041591) *
5) orbital_period_na=NA 555 247 0 (0.554954955 0.445045045)
10) star_sp_type_na=NA 262 10 0 (0.961832061 0.038167939) *
11) star_sp_type_na=!NA 293 56 1 (0.191126280 0.808873720) *
3) star_name_na=NA 780 52 1 (0.066666667 0.933333333) *
[[2]]$snipped.nodes
NULL
[[2]]$xlim
[1] 0 1
[[2]]$ylim
[1] 0 1
[[2]]$x
[1] 0.63576771 0.29894719 0.07440017 0.52349421 0.37379619 0.67319222 0.97258824
[[2]]$y
[1] 0.93177936 0.66221049 0.04220209 0.39264162 0.04220209 0.04220209 0.04220209
[[2]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
x 0.6357677 0.2989472 0.07440017 0.5234942 0.3737962 0.6731922 0.9725882
NA 0.2989472 0.07440017 0.5234942 0.3737962 0.6731922 0.9725882
NA 0.6357677 0.29894719 0.2989472 0.5234942 0.5234942 0.6357677
[[2]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
y 1.002619 0.7330497 0.1130413 0.4634808 0.1130413 0.1130413 0.1130413
NA 0.9792200 0.7096511 0.7096511 0.4400822 0.4400822 0.9792200
NA 0.9792200 0.7096511 0.7096511 0.4400822 0.4400822 0.9792200
[[2]]$labs
[1] "0\n\n6388 1030\n100%" "0\n\n6336 302\n89%" "0\n\n6028 55\n82%" "0\n\n308 247\n7%" "0\n\n252 10\n4%" "1\n\n56 237\n4%"
[7] "1\n\n52 728\n11%"
[[2]]$cex
[1] 0.5875
[[2]]$boxes
[[2]]$boxes$x1
[1] 0.62637727 0.28955674 0.06500972 0.51410376 0.36440575 0.66380177 0.96319780
[[2]]$boxes$y1
[1] 0.95582140 0.68625253 0.06624412 0.41668366 0.06624412 0.06624412 0.06624412
[[2]]$boxes$x2
[1] 0.64515816 0.30833763 0.08379061 0.53288465 0.38318664 0.68258266 0.98197869
[[2]]$boxes$y2
[1] 1.0026185 0.7330497 0.1130413 0.4634808 0.1130413 0.1130413 0.1130413
[[2]]$split.labs
[1] ""
[[2]]$split.cex
[1] 1 1 1 1 1 1 1
[[2]]$split.box
[[2]]$split.box$x1
[1] 0.22896365 -0.00843345 NA 0.29738762 NA NA NA
[[2]]$split.box$y1
[1] 0.8232100 0.5536411 NA 0.2840722 NA NA NA
[[2]]$split.box$x2
[1] 0.3689307 0.1572338 NA 0.4502048 NA NA NA
[[2]]$split.box$y2
[1] 0.8700071 0.6004383 NA 0.3308694 NA NA NA
[[3]]
[[3]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1266 0 (0.829334052 0.170665948)
2) mass_sini_na=NA 6011 138 0 (0.977042090 0.022957910)
4) omega_na=NA 5264 27 0 (0.994870821 0.005129179) *
5) omega_na=!NA 747 111 0 (0.851405622 0.148594378)
10) radius_na=!NA 598 16 0 (0.973244147 0.026755853) *
11) radius_na=NA 149 54 1 (0.362416107 0.637583893)
22) k_na=NA 59 13 0 (0.779661017 0.220338983) *
23) k_na=!NA 90 8 1 (0.088888889 0.911111111) *
3) mass_sini_na=!NA 1407 279 1 (0.198294243 0.801705757)
6) impact_parameter_na=!NA 115 6 0 (0.947826087 0.052173913) *
7) impact_parameter_na=NA 1292 170 1 (0.131578947 0.868421053)
14) eccentricity_na=NA 132 47 0 (0.643939394 0.356060606)
28) k_na=NA 89 12 0 (0.865168539 0.134831461) *
29) k_na=!NA 43 8 1 (0.186046512 0.813953488) *
15) eccentricity_na=!NA 1160 85 1 (0.073275862 0.926724138)
30) mag_v_na=NA 117 38 1 (0.324786325 0.675213675)
60) k_na=NA 45 9 0 (0.800000000 0.200000000) *
61) k_na=!NA 72 2 1 (0.027777778 0.972222222) *
31) mag_v_na=!NA 1043 47 1 (0.045062320 0.954937680) *
[[3]]$snipped.nodes
NULL
[[3]]$xlim
[1] 0 1
[[3]]$ylim
[1] 0 1
[[3]]$x
[1] 0.36678974 0.12818233 0.03666168 0.21970298 0.14125671 0.29814926 0.24585174 0.35044677 0.60539716 0.45504180 0.75575251 0.61193434 0.55963683 0.66423186
[15] 0.89957068 0.82112440 0.76882689 0.87342192 0.97801695
[[3]]$y
[1] 0.95299368 0.78499289 0.02898933 0.61699210 0.02898933 0.44899131 0.02898933 0.02898933 0.78499289 0.02898933 0.61699210 0.44899131 0.02898933 0.02898933
[15] 0.44899131 0.28099052 0.02898933 0.02898933 0.02898933
[[3]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.3667897 0.1281823 0.03666168 0.2197030 0.1412567 0.2981493 0.2458517 0.3504468 0.6053972 0.4550418 0.7557525 0.6119343 0.5596368 0.6642319 0.8995707
NA 0.1281823 0.03666168 0.2197030 0.1412567 0.2981493 0.2458517 0.3504468 0.6053972 0.4550418 0.7557525 0.6119343 0.5596368 0.6642319 0.8995707
NA 0.3667897 0.12818233 0.1281823 0.2197030 0.2197030 0.2981493 0.2981493 0.3667897 0.6053972 0.6053972 0.7557525 0.6119343 0.6119343 0.7557525
[,16] [,17] [,18] [,19]
x 0.8211244 0.7688269 0.8734219 0.9780169
0.8211244 0.7688269 0.8734219 0.9780169
0.8995707 0.8211244 0.8211244 0.8995707
[[3]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9999663 0.8319655 0.07596197 0.6639647 0.07596197 0.4959639 0.07596197 0.07596197 0.8319655 0.07596197 0.6639647 0.4959639 0.07596197 0.07596197 0.4959639
NA 0.9843673 0.81636648 0.8163665 0.64836569 0.6483657 0.48036490 0.48036490 0.9843673 0.81636648 0.8163665 0.6483657 0.48036490 0.48036490 0.6483657
NA 0.9843673 0.81636648 0.8163665 0.64836569 0.6483657 0.48036490 0.48036490 0.9843673 0.81636648 0.8163665 0.6483657 0.48036490 0.48036490 0.6483657
[,16] [,17] [,18] [,19]
y 0.3279632 0.07596197 0.07596197 0.07596197
0.4803649 0.31236411 0.31236411 0.48036490
0.4803649 0.31236411 0.31236411 0.48036490
[[3]]$labs
[1] "0\n\n6152 1266\n100%" "0\n\n5873 138\n81%" "0\n\n5237 27\n71%" "0\n\n636 111\n10%" "0\n\n582 16\n8%" "1\n\n54 95\n2%"
[7] "0\n\n46 13\n1%" "1\n\n8 82\n1%" "1\n\n279 1128\n19%" "0\n\n109 6\n2%" "1\n\n170 1122\n17%" "0\n\n85 47\n2%"
[13] "0\n\n77 12\n1%" "1\n\n8 35\n1%" "1\n\n85 1075\n16%" "1\n\n38 79\n2%" "0\n\n36 9\n1%" "1\n\n2 70\n1%"
[19] "1\n\n47 996\n14%"
[[3]]$cex
[1] 0.3875
[[3]]$boxes
[[3]]$boxes$x1
[1] 0.35937623 0.12076882 0.02924817 0.21228947 0.13384320 0.29073575 0.23843823 0.34303326 0.59798365 0.44762829 0.74833900 0.60452083 0.55222332 0.65681835
[15] 0.89215717 0.81371089 0.76141338 0.86600841 0.97060344
[[3]]$boxes$y1
[1] 0.96876822 0.80076743 0.04476387 0.63276664 0.04476387 0.46476585 0.04476387 0.04476387 0.80076743 0.04476387 0.63276664 0.46476585 0.04476387 0.04476387
[15] 0.46476585 0.29676506 0.04476387 0.04476387 0.04476387
[[3]]$boxes$x2
[1] 0.37420325 0.13559584 0.04407519 0.22711649 0.14867022 0.30556277 0.25326525 0.35786028 0.61281067 0.46245531 0.76316602 0.61934785 0.56705034 0.67164537
[15] 0.90698419 0.82853791 0.77624040 0.88083543 0.98543046
[[3]]$boxes$y2
[1] 0.99996632 0.83196553 0.07596197 0.66396474 0.07596197 0.49596395 0.07596197 0.07596197 0.83196553 0.07596197 0.66396474 0.49596395 0.07596197 0.07596197
[15] 0.49596395 0.32796316 0.07596197 0.07596197 0.07596197
[[3]]$split.labs
[1] ""
[[3]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[3]]$split.box
[[3]]$split.box$x1
[1] 0.080340483 -0.002283957 NA 0.101816839 NA 0.220250420 NA NA 0.386936357 NA 0.561127091 0.534035510
[13] NA NA 0.781684532 0.743225569 NA NA NA
[[3]]$split.box$y1
[1] 0.8806141 0.7126133 NA 0.5446125 NA 0.3766117 NA NA 0.7126133 NA 0.5446125 0.3766117 NA NA 0.3766117
[16] 0.2086109 NA NA NA
[[3]]$split.box$x2
[1] 0.17602418 0.07560732 NA 0.18069658 NA 0.27145306 NA NA 0.52314724 NA 0.66274160 0.58523815 NA NA
[15] 0.86056428 0.79442821 NA NA NA
[[3]]$split.box$y2
[1] 0.9118122 0.7438114 NA 0.5758106 NA 0.4078098 NA NA 0.7438114 NA 0.5758106 0.4078098 NA NA 0.4078098
[16] 0.2398090 NA NA NA
[[4]]
[[4]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 19 0 (0.997438663 0.002561337) *
[[4]]$snipped.nodes
NULL
[[4]]$xlim
[1] 0 1
[[4]]$ylim
[1] 0 1
[[4]]$x
[1] 0.5
[[4]]$y
[1] 0.5
[[4]]$branch.x
[,1]
x 0.5
NA
NA
[[4]]$branch.y
[,1]
y 0.5
NA
NA
[[4]]$labs
[1] "0\n\n7399 19\n100%"
[[4]]$cex
[1] 1
[[4]]$boxes
[[4]]$boxes$x1
[1] 0.4827018
[[4]]$boxes$y1
[1] 0.5413375
[[4]]$boxes$x2
[1] 0.5172982
[[4]]$boxes$y2
[1] 0.6193327
[[4]]$split.labs
[1] ""
[[4]]$split.cex
[1] 1
[[4]]$split.box
[[4]]$split.box$x1
[1] NA
[[4]]$split.box$y1
[1] NA
[[4]]$split.box$x2
[1] NA
[[4]]$split.box$y2
[1] NA
[[5]]
[[5]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 101 0 (0.98638447 0.01361553) *
[[5]]$snipped.nodes
NULL
[[5]]$xlim
[1] 0 1
[[5]]$ylim
[1] 0 1
[[5]]$x
[1] 0.5
[[5]]$y
[1] 0.5
[[5]]$branch.x
[,1]
x 0.5
NA
NA
[[5]]$branch.y
[,1]
y 0.5
NA
NA
[[5]]$labs
[1] "0\n\n7317 101\n100%"
[[5]]$cex
[1] 1
[[5]]$boxes
[[5]]$boxes$x1
[1] 0.4827018
[[5]]$boxes$y1
[1] 0.5413375
[[5]]$boxes$x2
[1] 0.5172982
[[5]]$boxes$y2
[1] 0.6193327
[[5]]$split.labs
[1] ""
[[5]]$split.cex
[1] 1
[[5]]$split.box
[[5]]$split.box$x1
[1] NA
[[5]]$split.box$y1
[1] NA
[[5]]$split.box$x2
[1] NA
[[5]]$split.box$y2
[1] NA
[[6]]
[[6]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 2890 1 (0.38959288 0.61040712)
2) radius_na=NA 2444 43 0 (0.98240589 0.01759411) *
3) radius_na=!NA 4974 489 1 (0.09831122 0.90168878)
6) orbital_period_na=NA 347 10 0 (0.97118156 0.02881844) *
7) orbital_period_na=!NA 4627 152 1 (0.03285066 0.96714934) *
[[6]]$snipped.nodes
NULL
[[6]]$xlim
[1] 0 1
[[6]]$ylim
[1] 0 1
[[6]]$x
[1] 0.40233205 0.07686754 0.72779656 0.51082022 0.94477290
[[6]]$y
[1] 0.90163928 0.06150195 0.51975867 0.06150195 0.06150195
[[6]]$branch.x
[,1] [,2] [,3] [,4] [,5]
x 0.4023321 0.07686754 0.7277966 0.5108202 0.9447729
NA 0.07686754 0.7277966 0.5108202 0.9447729
NA 0.40233205 0.4023321 0.7277966 0.7277966
[[6]]$branch.y
[,1] [,2] [,3] [,4] [,5]
y 1.009942 0.1698044 0.6280612 0.1698044 0.1698044
NA 0.9748439 0.9748439 0.5929633 0.5929633
NA 0.9748439 0.9748439 0.5929633 0.5929633
[[6]]$labs
[1] "1\n\n2890 4528\n100%" "0\n\n2401 43\n33%" "1\n\n489 4485\n67%" "0\n\n337 10\n5%" "1\n\n152 4475\n62%"
[[6]]$cex
[1] 0.9148437
[[6]]$boxes
[[6]]$boxes$x1
[1] 0.38651656 0.06105205 0.71198107 0.49500473 0.92895742
[[6]]$boxes$y1
[1] 0.93974605 0.09960872 0.55786544 0.09960872 0.09960872
[[6]]$boxes$x2
[1] 0.41814754 0.09268303 0.74361205 0.52663571 0.96058839
[[6]]$boxes$y2
[1] 1.0099418 0.1698044 0.6280612 0.1698044 0.1698044
[[6]]$split.labs
[1] ""
[[6]]$split.cex
[1] 1 1 1 1 1
[[6]]$split.box
[[6]]$split.box$x1
[1] -0.01169919 NA 0.37826667 NA NA
[[6]]$split.box$y1
[1] 0.7387852 NA 0.3569046 NA NA
[[6]]$split.box$x2
[1] 0.1654343 NA 0.6433738 NA NA
[[6]]$split.box$y2
[1] 0.8089809 NA 0.4271003 NA NA
[[7]]
[[7]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 313 0 (0.957805338 0.042194662)
2) orbital_period_na=!NA 6125 8 0 (0.998693878 0.001306122) *
3) orbital_period_na=NA 1293 305 0 (0.764114462 0.235885538)
6) alternate_names_na=!NA 771 38 0 (0.950713359 0.049286641)
12) star_name_na=NA 562 2 0 (0.996441281 0.003558719) *
13) star_name_na=!NA 209 36 0 (0.827751196 0.172248804)
26) star_sp_type_na=!NA 171 4 0 (0.976608187 0.023391813) *
27) star_sp_type_na=NA 38 6 1 (0.157894737 0.842105263) *
7) alternate_names_na=NA 522 255 1 (0.488505747 0.511494253)
14) semi_major_axis_na=NA 184 31 0 (0.831521739 0.168478261)
28) star_distance_NA=!NA 172 23 0 (0.866279070 0.133720930)
56) star_mass_na=NA 161 15 0 (0.906832298 0.093167702) *
57) star_mass_na=!NA 11 3 1 (0.272727273 0.727272727) *
29) star_distance_NA=NA 12 4 1 (0.333333333 0.666666667) *
15) semi_major_axis_na=!NA 338 102 1 (0.301775148 0.698224852)
30) star_sp_type_na=!NA 113 21 0 (0.814159292 0.185840708)
60) mag_v_na=!NA 70 1 0 (0.985714286 0.014285714) *
61) mag_v_na=NA 43 20 0 (0.534883721 0.465116279)
122) mag_k_na=!NA 11 0 0 (1.000000000 0.000000000) *
123) mag_k_na=NA 32 12 1 (0.375000000 0.625000000) *
31) star_sp_type_na=NA 225 10 1 (0.044444444 0.955555556) *
[[7]]$snipped.nodes
NULL
[[7]]$xlim
[1] 0 1
[[7]]$ylim
[1] 0 1
[[7]]$x
[1] 0.24557270 0.03657107 0.45457432 0.20258655 0.13143706 0.27373603 0.22630304 0.32116903 0.70656208 0.53461749 0.46346800 0.41603501 0.51090099 0.60576698
[15] 0.87850668 0.77178245 0.70063296 0.84293194 0.79549894 0.89036493 0.98523091
[[7]]$y
[1] 0.96489496 0.02130589 0.82192692 0.67895888 0.02130589 0.53599084 0.02130589 0.02130589 0.67895888 0.53599084 0.39302280 0.02130589 0.02130589 0.02130589
[15] 0.53599084 0.39302280 0.02130589 0.25005475 0.02130589 0.02130589 0.02130589
[[7]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
x 0.2455727 0.03657107 0.4545743 0.2025865 0.1314371 0.2737360 0.226303 0.321169 0.7065621 0.5346175 0.4634680 0.416035 0.510901 0.6057670 0.8785067 0.7717824
NA 0.03657107 0.4545743 0.2025865 0.1314371 0.2737360 0.226303 0.321169 0.7065621 0.5346175 0.4634680 0.416035 0.510901 0.6057670 0.8785067 0.7717824
NA 0.24557270 0.2455727 0.4545743 0.2025865 0.2025865 0.273736 0.273736 0.4545743 0.7065621 0.5346175 0.463468 0.463468 0.5346175 0.7065621 0.8785067
[,17] [,18] [,19] [,20] [,21]
x 0.7006330 0.8429319 0.7954989 0.8903649 0.9852309
0.7006330 0.8429319 0.7954989 0.8903649 0.9852309
0.7717824 0.7717824 0.8429319 0.8429319 0.8785067
[[7]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
y 0.9991739 0.0555848 0.8562058 0.7132378 0.0555848 0.5702697 0.0555848 0.0555848 0.7132378 0.5702697 0.4273017 0.0555848 0.0555848 0.0555848 0.5702697 0.4273017
NA 0.9874746 0.9874746 0.8445065 0.7015385 0.7015385 0.5585705 0.5585705 0.8445065 0.7015385 0.5585705 0.4156024 0.4156024 0.5585705 0.7015385 0.5585705
NA 0.9874746 0.9874746 0.8445065 0.7015385 0.7015385 0.5585705 0.5585705 0.8445065 0.7015385 0.5585705 0.4156024 0.4156024 0.5585705 0.7015385 0.5585705
[,17] [,18] [,19] [,20] [,21]
y 0.0555848 0.2843337 0.0555848 0.0555848 0.0555848
0.4156024 0.4156024 0.2726344 0.2726344 0.5585705
0.4156024 0.4156024 0.2726344 0.2726344 0.5585705
[[7]]$labs
[1] "0\n\n7105 313\n100%" "0\n\n6117 8\n83%" "0\n\n988 305\n17%" "0\n\n733 38\n10%" "0\n\n560 2\n8%" "0\n\n173 36\n3%"
[7] "0\n\n167 4\n2%" "1\n\n6 32\n1%" "1\n\n255 267\n7%" "0\n\n153 31\n2%" "0\n\n149 23\n2%" "0\n\n146 15\n2%"
[13] "1\n\n3 8\n0%" "1\n\n4 8\n0%" "1\n\n102 236\n5%" "0\n\n92 21\n2%" "0\n\n69 1\n1%" "0\n\n23 20\n1%"
[19] "0\n\n11 0\n0%" "1\n\n12 20\n0%" "1\n\n10 215\n3%"
[[7]]$cex
[1] 0.275
[[7]]$boxes
[[7]]$boxes$x1
[1] 0.24063036 0.03162873 0.44963198 0.19764421 0.12649472 0.26879369 0.22136070 0.31622669 0.70161975 0.52967515 0.45852566 0.41109267 0.50595865 0.60082464
[15] 0.87356434 0.76684011 0.69569062 0.83798960 0.79055660 0.88542259 0.98028857
[[7]]$boxes$y1
[1] 0.97577530 0.03218622 0.83280726 0.68983922 0.03218622 0.54687117 0.03218622 0.03218622 0.68983922 0.54687117 0.40390313 0.03218622 0.03218622 0.03218622
[15] 0.54687117 0.40390313 0.03218622 0.26093509 0.03218622 0.03218622 0.03218622
[[7]]$boxes$x2
[1] 0.25051503 0.04151341 0.45951666 0.20752889 0.13637940 0.27867837 0.23124538 0.32611137 0.71150442 0.53955983 0.46841034 0.42097735 0.51584333 0.61070932
[15] 0.88344902 0.77672479 0.70557530 0.84787428 0.80044128 0.89530727 0.99017325
[[7]]$boxes$y2
[1] 0.9991739 0.0555848 0.8562058 0.7132378 0.0555848 0.5702697 0.0555848 0.0555848 0.7132378 0.5702697 0.4273017 0.0555848 0.0555848 0.0555848 0.5702697
[16] 0.4273017 0.0555848 0.2843337 0.0555848 0.0555848 0.0555848
[[7]]$split.labs
[1] ""
[[7]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[7]]$split.box
[[7]]$split.box$x1
[1] -0.001979176 NA 0.159588190 0.098817615 NA 0.189729727 NA NA 0.490630665 0.424423517 0.383415567 NA
[13] NA NA 0.735209134 0.673450092 NA 0.768810310 NA NA NA
[[7]]$split.box$y1
[1] 0.9106103 NA 0.7676422 0.6246742 NA 0.4817061 NA NA 0.6246742 0.4817061 0.3387381 NA NA NA 0.4817061
[16] 0.3387381 NA 0.1957701 NA NA NA
[[7]]$split.box$x2
[1] 0.07512133 NA 0.24558490 0.16405650 NA 0.26287636 NA NA 0.57860431 0.50251249 0.44865445 NA NA NA
[15] 0.80835576 0.72781583 NA 0.82218758 NA NA NA
[[7]]$split.box$y2
[1] 0.9340088 NA 0.7910408 0.6480728 NA 0.5051047 NA NA 0.6480728 0.5051047 0.3621367 NA NA NA 0.5051047
[16] 0.3621367 NA 0.2191686 NA NA NA
[[8]]
[[8]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 34 0 (0.995416554 0.004583446)
2) mass_measurement_type_na=NA 4320 6 0 (0.998611111 0.001388889) *
3) mass_measurement_type_na=!NA 3098 28 0 (0.990961911 0.009038089)
6) mag_h_na=NA 2403 7 0 (0.997086975 0.002913025) *
7) mag_h_na=!NA 695 21 0 (0.969784173 0.030215827)
14) semi_major_axis_na=!NA 638 11 0 (0.982758621 0.017241379) *
15) semi_major_axis_na=NA 57 10 0 (0.824561404 0.175438596)
30) tconj_na=!NA 31 0 0 (1.000000000 0.000000000) *
31) tconj_na=NA 26 10 0 (0.615384615 0.384615385)
62) star_sp_type_na=!NA 15 3 0 (0.800000000 0.200000000) *
63) star_sp_type_na=NA 11 4 1 (0.363636364 0.636363636) *
[[8]]$snipped.nodes
NULL
[[8]]$xlim
[1] 0 1
[[8]]$ylim
[1] 0 1
[[8]]$x
[1] 0.2534729 0.0772349 0.4297109 0.2591580 0.6002637 0.4410811 0.7594464 0.6230041 0.8958887 0.8049272 0.9868503
[[8]]$y
[1] 0.95299368 0.02898933 0.78499289 0.02898933 0.61699210 0.02898933 0.44899131 0.02898933 0.28099052 0.02898933 0.02898933
[[8]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
x 0.2534729 0.0772349 0.4297109 0.2591580 0.6002637 0.4410811 0.7594464 0.6230041 0.8958887 0.8049272 0.9868503
NA 0.0772349 0.4297109 0.2591580 0.6002637 0.4410811 0.7594464 0.6230041 0.8958887 0.8049272 0.9868503
NA 0.2534729 0.2534729 0.4297109 0.4297109 0.6002637 0.6002637 0.7594464 0.7594464 0.8958887 0.8958887
[[8]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
y 0.9999663 0.07596197 0.8319655 0.07596197 0.6639647 0.07596197 0.4959639 0.07596197 0.3279632 0.07596197 0.07596197
NA 0.98436727 0.9843673 0.81636648 0.8163665 0.64836569 0.6483657 0.48036490 0.4803649 0.31236411 0.31236411
NA 0.98436727 0.9843673 0.81636648 0.8163665 0.64836569 0.6483657 0.48036490 0.4803649 0.31236411 0.31236411
[[8]]$labs
[1] "0\n\n7384 34\n100%" "0\n\n4314 6\n58%" "0\n\n3070 28\n42%" "0\n\n2396 7\n32%" "0\n\n674 21\n9%" "0\n\n627 11\n9%" "0\n\n47 10\n1%"
[8] "0\n\n31 0\n0%" "0\n\n16 10\n0%" "0\n\n12 3\n0%" "1\n\n4 7\n0%"
[[8]]$cex
[1] 0.3875
[[8]]$boxes
[[8]]$boxes$x1
[1] 0.24605937 0.06982139 0.42229735 0.25174446 0.59285024 0.43366754 0.75203293 0.61559062 0.88847524 0.79751370 0.97943678
[[8]]$boxes$y1
[1] 0.96876822 0.04476387 0.80076743 0.04476387 0.63276664 0.04476387 0.46476585 0.04476387 0.29676506 0.04476387 0.04476387
[[8]]$boxes$x2
[1] 0.26088639 0.08464841 0.43712437 0.26657148 0.60767726 0.44849456 0.76685995 0.63041764 0.90330226 0.81234072 0.99426380
[[8]]$boxes$y2
[1] 0.99996632 0.07596197 0.83196553 0.07596197 0.66396474 0.07596197 0.49596395 0.07596197 0.32796316 0.07596197 0.07596197
[[8]]$split.labs
[1] ""
[[8]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1
[[8]]$split.box
[[8]]$split.box$x1
[1] -0.007674503 NA 0.220212336 NA 0.374458311 NA 0.586529662 NA 0.749671849 NA NA
[[8]]$split.box$y1
[1] 0.8806141 NA 0.7126133 NA 0.5446125 NA 0.3766117 NA 0.2086109 NA NA
[[8]]$split.box$x2
[1] 0.1621443 NA 0.2981036 NA 0.5077038 NA 0.6594786 NA 0.8601826 NA NA
[[8]]$split.box$y2
[1] 0.9118122 NA 0.7438114 NA 0.5758106 NA 0.4078098 NA 0.2398090 NA NA
[[9]]
[[9]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 178 0 (0.976004314 0.023995686)
2) star_metallicity_na=!NA 5233 14 0 (0.997324670 0.002675330) *
3) star_metallicity_na=NA 2185 164 0 (0.924942792 0.075057208)
6) mass_sini_na=NA 1873 53 0 (0.971703150 0.028296850)
12) star_sp_type_na=NA 1348 5 0 (0.996290801 0.003709199) *
13) star_sp_type_na=!NA 525 48 0 (0.908571429 0.091428571)
26) star_teff_na=!NA 367 15 0 (0.959128065 0.040871935) *
27) star_teff_na=NA 158 33 0 (0.791139241 0.208860759)
54) orbital_period_na=NA 87 1 0 (0.988505747 0.011494253) *
55) orbital_period_na=!NA 71 32 0 (0.549295775 0.450704225)
110) mass_measurement_type_na=!NA 36 8 0 (0.777777778 0.222222222) *
111) mass_measurement_type_na=NA 35 11 1 (0.314285714 0.685714286)
222) alternate_names_na=!NA 10 4 0 (0.600000000 0.400000000) *
223) alternate_names_na=NA 25 5 1 (0.200000000 0.800000000) *
7) mass_sini_na=!NA 312 111 0 (0.644230769 0.355769231)
14) k_na=!NA 157 3 0 (0.980891720 0.019108280) *
15) k_na=NA 155 47 1 (0.303225806 0.696774194)
30) mag_v_na=!NA 60 24 0 (0.600000000 0.400000000)
60) angular_distance_na=!NA 15 0 0 (1.000000000 0.000000000) *
61) angular_distance_na=NA 45 21 1 (0.466666667 0.533333333)
122) star_mass_na=NA 11 3 0 (0.727272727 0.272727273) *
123) star_mass_na=!NA 34 13 1 (0.382352941 0.617647059) *
31) mag_v_na=NA 95 11 1 (0.115789474 0.884210526) *
[[9]]$snipped.nodes
NULL
[[9]]$xlim
[1] 0 1
[[9]]$ylim
[1] 0 1
[[9]]$x
[1] 0.26280996 0.03844651 0.48717340 0.20823506 0.12468832 0.29178181 0.21093012 0.37263350 0.29717192 0.44809508 0.38341373 0.51277643 0.46965553 0.55589733
[15] 0.76611173 0.64213914 0.89008432 0.79306229 0.72838094 0.85774365 0.81462275 0.90086455 0.98710635
[[9]]$y
[1] 0.96623316 0.01992797 0.84333638 0.72043960 0.01992797 0.59754283 0.01992797 0.47464605 0.01992797 0.35174927 0.01992797 0.22885249 0.01992797 0.01992797
[15] 0.72043960 0.01992797 0.59754283 0.47464605 0.01992797 0.35174927 0.01992797 0.01992797 0.01992797
[[9]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
x 0.26281 0.03844651 0.4871734 0.2082351 0.1246883 0.2917818 0.2109301 0.3726335 0.2971719 0.4480951 0.3834137 0.5127764 0.4696555 0.5558973 0.7661117 0.6421391
NA 0.03844651 0.4871734 0.2082351 0.1246883 0.2917818 0.2109301 0.3726335 0.2971719 0.4480951 0.3834137 0.5127764 0.4696555 0.5558973 0.7661117 0.6421391
NA 0.26280996 0.2628100 0.4871734 0.2082351 0.2082351 0.2917818 0.2917818 0.3726335 0.3726335 0.4480951 0.4480951 0.5127764 0.5127764 0.4871734 0.7661117
[,17] [,18] [,19] [,20] [,21] [,22] [,23]
x 0.8900843 0.7930623 0.7283809 0.8577436 0.8146227 0.9008645 0.9871064
0.8900843 0.7930623 0.7283809 0.8577436 0.8146227 0.9008645 0.9871064
0.7661117 0.8900843 0.7930623 0.7930623 0.8577436 0.8577436 0.8900843
[[9]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9989912 0.05268598 0.8760944 0.7531976 0.05268598 0.6303008 0.05268598 0.5074041 0.05268598 0.3845073 0.05268598 0.2616105 0.05268598 0.05268598 0.7531976
NA 0.98729187 0.9872919 0.8643951 0.74149832 0.7414983 0.61860154 0.6186015 0.49570477 0.4957048 0.37280799 0.3728080 0.24991121 0.24991121 0.8643951
NA 0.98729187 0.9872919 0.8643951 0.74149832 0.7414983 0.61860154 0.6186015 0.49570477 0.4957048 0.37280799 0.3728080 0.24991121 0.24991121 0.8643951
[,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23]
y 0.05268598 0.6303008 0.5074041 0.05268598 0.3845073 0.05268598 0.05268598 0.05268598
0.74149832 0.7414983 0.6186015 0.49570477 0.4957048 0.37280799 0.37280799 0.61860154
0.74149832 0.7414983 0.6186015 0.49570477 0.4957048 0.37280799 0.37280799 0.61860154
[[9]]$labs
[1] "0\n\n7240 178\n100%" "0\n\n5219 14\n71%" "0\n\n2021 164\n29%" "0\n\n1820 53\n25%" "0\n\n1343 5\n18%" "0\n\n477 48\n7%"
[7] "0\n\n352 15\n5%" "0\n\n125 33\n2%" "0\n\n86 1\n1%" "0\n\n39 32\n1%" "0\n\n28 8\n0%" "1\n\n11 24\n0%"
[13] "0\n\n6 4\n0%" "1\n\n5 20\n0%" "0\n\n201 111\n4%" "0\n\n154 3\n2%" "1\n\n47 108\n2%" "0\n\n36 24\n1%"
[19] "0\n\n15 0\n0%" "1\n\n21 24\n1%" "0\n\n8 3\n0%" "1\n\n13 21\n0%" "1\n\n11 84\n1%"
[[9]]$cex
[1] 0.25
[[9]]$boxes
[[9]]$boxes$x1
[1] 0.25786762 0.03350417 0.48223106 0.20329272 0.11974598 0.28683947 0.20598778 0.36769116 0.29222958 0.44315274 0.37847139 0.50783409 0.46471319 0.55095499
[15] 0.76116939 0.63719680 0.88514198 0.78811995 0.72343860 0.85280131 0.80968041 0.89592221 0.98216401
[[9]]$boxes$y1
[1] 0.9755926 0.0292874 0.8526958 0.7297990 0.0292874 0.6069023 0.0292874 0.4840055 0.0292874 0.3611087 0.0292874 0.2382119 0.0292874 0.0292874 0.7297990
[16] 0.0292874 0.6069023 0.4840055 0.0292874 0.3611087 0.0292874 0.0292874 0.0292874
[[9]]$boxes$x2
[1] 0.26775230 0.04338885 0.49211574 0.21317740 0.12963066 0.29672415 0.21587246 0.37757584 0.30211426 0.45303742 0.38835607 0.51771877 0.47459787 0.56083967
[15] 0.77105407 0.64708148 0.89502666 0.79800463 0.73332328 0.86268599 0.81956508 0.90580689 0.99204869
[[9]]$boxes$y2
[1] 0.99899116 0.05268598 0.87609438 0.75319761 0.05268598 0.63030083 0.05268598 0.50740405 0.05268598 0.38450728 0.05268598 0.26161050 0.05268598 0.05268598
[15] 0.75319761 0.05268598 0.63030083 0.50740405 0.05268598 0.38450728 0.05268598 0.05268598 0.05268598
[[9]]$split.labs
[1] ""
[[9]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[9]]$split.box
[[9]]$split.box$x1
[1] -0.002080672 NA 0.176109856 0.088609237 NA 0.181770316 NA 0.259115908 NA 0.326082586 NA 0.426657175
[13] NA NA 0.624346715 NA 0.765879425 0.684394117 NA 0.782003302 NA NA NA
[[9]]$split.box$y1
[1] 0.9119485 NA 0.7890517 0.6661549 NA 0.5432581 NA 0.4203614 NA 0.2974646 NA 0.1745678 NA NA 0.6661549
[16] NA 0.5432581 0.4203614 NA 0.2974646 NA NA NA
[[9]]$split.box$x2
[1] 0.0789737 NA 0.2403603 0.1607674 NA 0.2400899 NA 0.3352279 NA 0.4407449 NA 0.5126539 NA NA 0.6599316
[16] NA 0.8202452 0.7723678 NA 0.8472422 NA NA NA
[[9]]$split.box$y2
[1] 0.9353470 NA 0.8124503 0.6895535 NA 0.5666567 NA 0.4437599 NA 0.3208632 NA 0.1979664 NA NA 0.6895535
[16] NA 0.5666567 0.4437599 NA 0.3208632 NA NA 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